Data Neighbor Podcast

In this episode of Data Neighbor, we’re joined by Nate Nichols, VP of Product at Tableau, to unpack how AI is changing analytics, not in theory, but in practice.🔗 Links & ResourcesData Neighbor: https://dataneighbor.comAI Analytics for Builders (course): https://bit.ly/analyst-aiFree 30-day AI Evals course: https://dataneighbor.kit.com/b595040b93Tableau Blog: https://www.tableau.com/blogConnect with the team (tell us YouTube sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rConnect with Nate:https://www.linkedin.com/in/nate-nichols/Nate walks us through Tableau’s shift to proactive, agentic analytics: systems that detect meaningful changes, run diagnostic analysis, surface context, and push insights directly into the tools teams already use.We talk about:- Why the “last mile” of analytics has always been broken- How semantic models unlock trustworthy AI-driven analysis- What agentic analytics actually looks like in production- Where human judgment still matters as AI takes on more of the workflow- How analyst roles are evolving as insight moves closer to actionThis conversation changed how we think about the timeline for agentic analytics. It’s not years away, it’s already here.Chapters:00:00 Introduction to AI and data analytics00:31 Nate Nichols’ journey in AI and analytics00:59 The evolution of analytics and Tableau’s role02:42 Understanding user needs and data familiarity04:52 The pressure on analysts and business users07:33 The analytics pipeline and where value is lost10:16 Using AI to drive real outcomes13:03 Measuring success beyond dashboards15:49 What generative AI unlocks for analytics18:24 Trust, governance, and AI-driven insights24:36 Rolling out AI without breaking trust26:25 GenAI features inside Tableau28:10 Live demo: AI-powered analytics in Tableau33:47 Proactive analytics, alerts, and agents39:31 The future of analytics and human judgment57:19 Outro#dataneighbor #analytics #ai #agenticanalytics #tableau #datascience #dataanalytics #aievals #productanalytics

In this episode of Data Neighbor, we’re joined by Rami Abi Habib, CEO and co-founder of Querio, to talk about what agentic analytics looks like when it’s built into a BI product from the ground up.🔗 Links & ResourcesCheck out Querio: https://querio.ai/A ➞ B Podcast:  @QuerioData  Free AI workshops schedule: https://dataneighbor.comAI Analytics for Builders (course): https://bit.ly/analyst-aiFree 30-day AI Evals course: https://dataneighbor.kit.com/b595040b93Connect with the team (tell us YouTube sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rConnect with Rami: https://www.linkedin.com/in/datarami/We talk about:* Why dashboards became the wrong default for “data-driven” teams* How agentic analytics changes the ticketing model for data teams* Why “BI as code” matters for transparency and trust* The semantic layer and context flywheel: how systems improve week over week* Where human judgment still matters (metrics, definitions, exceptions, executive trust)* What self-serve analytics does to data literacy across the business* What it will take to reach a “prompt is the dashboard” worldChapters:00:00 Introduction to Data Analysis in the AI Era04:18 Rami's Journey and Insights from Amazon09:08 The Birth of Querio: Addressing BI Challenges12:28 Understanding Querio: A New Approach to Data13:26 Governance and Trust in Data Analytics16:14 Workflow and User Experience with Querio22:53 Future Innovations and Enhancements in Querio25:47 Understanding Data Context and Metrics27:27 The Role of Documentation in Data Management28:46 Pilot Programs and Customer Onboarding29:49 The Shift Towards Self-Service Data31:58 Rethinking Dashboards and Data Access36:51 The Future of Data Tools and AI Integration38:33 Building a Strong Data Layer41:32 Challenges in Data Integration and Management46:38 Outro#dataneighbor #analytics #ai #agenticanalytics #querio #datascience #dataanalytics #productanalytics #aievals

Become and Expert in Agentic Analytics and AI Evals at https://dataneighbor.com/Learn more about Index:https://index.app/In this episode of the Data Neighbor Podcast, we sit down with Xavier Pladevall, co-founder of Index, to break down what’s actually changing in business intelligence and what’s not as AI enters the analytics stack.Index is building an AI-powered data platform that blends dashboards, SQL, and an agentic chat experience. But as Xavier explains, the hard parts of data work still show up in the same places: messy data, unclear questions, and getting a room of humans to trust and act on what they see.In this episode, you’ll learn:- Why most “modern data” talk is disconnected from what teams actually use day to day- Why dashboards remain the default interface for stakeholders- How product design and UI quality change trust in data- What Index 1.0 is solving today, and what Index 2.0 unlocks next- How Index 2.0 makes analytics more proactive with recommended questions- Where AI breaks in analytics and why data quality is still the bottleneck- Why “data slop” compounds AI errors in surprising ways- Why data scientists are not going anywhere, even as AI takes on more tasks- Why SQL is more important than ever, just more abstractedWhat the real future looks like beyond “chat with your data”#datapodcast #analytics #businessintelligence #datatools #datascience #aiforanalytics #agenticanalytics #productanalytics #dataleadership #dataneighbor #index

Explore Data Neighbor free workshops on AI evaluation and agentic analytics (plus our live, hands on course): https://dataneighbor.com/Learn more about LiveDocs:https://livedocs.com/In this episode of the Data Neighbor Podcast, we sit down with Arsalan Bashir, founder and CEO of LiveDocs, to unpack one of the biggest shifts happening in data right now: tools that don’t just help you analyze data, but start doing parts of the analysis for you.If you care about the future of analytics workflows, this is a must watch.Connect with the team (tell us YouTube sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rConnect with Arsalan:https://www.linkedin.com/in/arslnb/In this episode, you’ll learn:- Why “chat with your data” is the wrong mental model for where analytics is headed- What an “AI data scientist” actually means in practice (and what it doesn’t)- Why context matters more than schema for getting correct results from AI-generated analysis- How LiveDocs blends notebooks, app-building, and agentic workflows in one workspace- What it looks like when AI automates the 0→80% of data work (aka “yak shaving”)- How scheduling, notifications, and lightweight automation change the day-to-day of analysis- Where AI can go wrong in data work, and how to design for trust and review- Why handoff between stakeholders and data teams is still the core bottleneck- What new skills will matter most for analysts and data teams as agentic tools become standard#datapodcast #analytics #businessintelligence #datatools #datascience #aiforanalytics #agents #agenticanalytics #productanalytics #dataleadership #dataneighbor #livedocs

Explore Data Neighbor free workshops on AI evaluation and agentic analytics (plus our live, hands on course): https://dataneighbor.com/Learn more about Count:https://count.co/In this episode of the Data Neighbor Podcast, we sit down with Ollie Hughes, co founder and CEO of Count, to unpack why traditional BI often fails at the real job teams hire data for: making better decisions, faster.If you care about the future of analytics workflows, this is a must watch.Connect with the team (tell us YouTube sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rConnect with Ollie:https://www.linkedin.com/in/hughesoliver/In this episode, you’ll learn:- Why dashboards are not designed for collaborative decision making- What the “service trap” is and how it keeps data teams in reactive work- How operational clarity helps companies simplify what matters- Why a canvas based interface changes how teams reason with data- What collaborative analysis looks like in practice between analysts and stakeholders- How agile decision making can reduce time to insight and time to decision- How Count thinks about AI agents for analytics without black boxes- What skills will matter most for data teams as AI takes on more of the mechanical work#datapodcast #analytics #businessintelligence #datatools #datascience #aiforanalytics #agents #agenticanalytics #aievals #productanalytics #dataleadership #dataneighbor #count

Join Our Upcoming AI Evals Cohort! https://maven.com/dataneighbor/ai-evalsSubscribe to our newsletter: https://dataneighbor.substack.com/DNP Swag Store: https://shop.dataneighbor.com/In this episode of the Data Neighbor Podcast, we do something different. No guest. Just the three of us reflecting on what we learned after a year of running the show.We talk honestly about what surprised us most, what turned out to be harder than expected, and why the “real work” of podcasting is everything around the recording: editing, publishing, distribution, and yes, thumbnails.We also dig into what hosting a podcast has done for our careers: better communication reps, more surface area in the industry, and a faster growing network than we expected.Finally, we share a preview of what we are building in 2026, including two big themes we keep seeing across the industry: AI product evaluation and AI powered analytics.If you are thinking about starting a podcast, already building one, or just want a candid behind the scenes look at what it takes to stay consistent for a full year, this episode is for you.Connect with the team (tell us which platform sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rIn this episode, you’ll learn:- What nobody tells you about the time investment behind a podcast- Why consistency is the hardest part, and how to design for it- How having multiple co hosts reduces burnout and improves the show- How podcasting can expand your network and professional opportunities- What we learned from a year of interviewing builders and operators- What we are focusing on in 2026: AI evaluation and AI powered analytics#podcasting #podcast #creator #contentcreation #careergrowth #networking #aiproduct #aievaluation #analytics #datascience #dataneighbor

In this episode of the Data Neighbor Podcast, we sit down with Shelby Heinecke, PhD, Senior AI Research Manager at Salesforce, to break down what modern AI teams actually look like and how enterprise AI gets built in practice.Shelby shares how her team moves research into production, why small crisp problem definitions outperform ambitious abstractions, and how evaluation before development has become a non negotiable part of the workflow.We also talk about the shifting shape of AI teams, the rising importance of domain experts, and why interdisciplinary collaboration is quickly becoming the core of the field.If you want an inside look at how leading AI orgs actually operate, this is the episode.Connect with the team (tell us which platform sent you!):- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Shane Butler: https://linkedin.openinapp.co/b02fe - Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Shelby:https://www.linkedin.com/in/shelby-heinecke/In this episode, you’ll learn:- How enterprise AI research teams actually set direction- Why crisp scope and early evaluation decide which projects reach production- What makes interdisciplinary collaboration essential for AI success- How small models and agents are being deployed across Salesforce- What skills matter most for the next generation of AI roles- Why embodied agents may represent the next major leap in AI#aipodcast #airesearch #salesforce #aiteams #aiproducts #llm #datascience #mlengineering #aidevelopment #agents #embodiedai #dataneighbor #aifuture

Join us for an inspiring conversation with Sadie St Lawrence, founder and CEO of Women in Data, and the Human Machine Collaboration Institute! Sadie shares her incredible journey from piano and neuroscience to pioneering a global movement empowering tens of thousands of women in data and AI. Discover her unique insights on building impactful communities, navigating career changes, and the evolving role of humans in the age of AI.In this episode, we cover:- Sadie's fascinating career trajectory, from a neuroscience lab to a data science pioneer and community builder.- The origin story of Women in Data, starting from a personal need for community to a global movement of 70,000 members across 120+ countries.- The critical role of consistency and trust in building a thriving community and achieving professional growth.- The current landscape of diversity in data careers, with eye-opening statistics and the significant impact of female leadership.- Sadie's visionary perspective on the future of work, where humans become "orchestra conductors" in a world augmented by AI.- The mission of the Human Machine Collaboration Institute (HMCI) in tackling fundamental questions about humanity, emotion, and consciousness in the AI era.- Practical advice on cultivating curiosity, breaking patterns, and leveraging your innate desire to learn for career advancement and personal fulfillment.Whether you're looking to start a community, advance your career in data, or curious about the philosophical implications of AI, Sadie's story and insights offer invaluable lessons. Tune in to understand why consistency, community, and curiosity are your greatest assets in the rapidly changing world of technology.Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#DataScience #AI #WomenInData #CareerAdvice #TechLeadership #CommunityBuilding #HumanMachineCollaboration #Curiosity #DiversityInTech #Neuroscience #Consciousness #FutureOfWork #DataCareers #STEM #ProfessionalDevelopment #Podcast #DataNeighbor

In this episode of the Data Neighbor Podcast, we sit down with Lei Tang, co-founder and CTO of Fabi AI, to explore the messy reality of data quality, the limits of self-serve BI, and why Vibe Analytics might be the shift organizations need. With experience leading data science at Lyft, Walmart Labs, and Clari, Lei brings grounded, first-hand insights into how modern data teams can thrive even when their data is anything but clean.You’ll learn:- Why “perfect data” is a myth and what to do instead- How AI-native BI changes the self-serve equation- The challenges and promise of Vibe Analytics- Why critical thinking, not SQL, is your most valuable skill- The case for AI-driven semantic layers over manual curation- How AI agents might evolve into collaborative teammates- Real risks of AI hallucinations and how to build guardrailsIf you’ve ever dealt with stakeholder overload, a graveyard of unused dashboards, or felt stuck waiting on a “single source of truth” project to finish, this one’s for you. We get real about trade-offs, show how AI can amplify impact (not replace you), and dive into what the future of analytics workflows might actually look like.Connect with Lei: https://www.linkedin.com/in/lei-tang-ai/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#datascience #vibeanalytics #fabi #selfservebi #aiinanalytics #dataquality #dataengineering #dataops #aibi #dataneighborpodcast #aiproducts #dataworkflows #analyticsleadership #futureofanalytics

AI is evolving faster than ever, and the people keeping up with it are the AI Research Engineers. In this episode of the Data Neighbor Podcast, we sit down with Sandi Besen, AI Research Engineer at IBM Research, to unpack what it actually means to live and work on the bleeding edge of AI.Sandi shares what it takes to move from model demos to real systems, why research engineering is becoming one of the most critical jobs in tech, and how she prototypes, evaluates, and ships new agent frameworks at record speed.Connect with the team (tell us which platform sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Sandi: https://www.linkedin.com/in/sandibesen/In this episode, you’ll learn about:-What an AI Research Engineer actually does day-to-day-How research engineering bridges AI research and production-Why requirements frameworks help agents stay reliable-The trade-offs between low-code and pro-code approaches-How evals and observability are evolving for agent systems-The human side of working at the frontier of AI#aipodcast #airesearch #ibmresearch #aiagents #agentframeworks #llm #datascience #mlengineering #automation #aidevelopment #beeai #aiproducts #researchengineering #dataneighbor #aifuture #ibm

AI is moving fast, but reliable agents are still rare. In this Data Neighbor Podcast, we sit down with Jigyasa Grover, ML Engineer at Uber, author of Sculpting Data for ML: The first act of Machine Learning, and member of Google’s ML Advisory Board, to unpack why most AI agents fail and what it really takes to build ones you can count on.Jigyasa shares how to design, evaluate, and secure reliable agent systems - from memory management and adversarial testing to using human judgment without slowing down innovation.Connect with the team (tell us YouTube sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Jigyasa: https://www.linkedin.com/in/jigyasa-grover/In this episode, Jigyasa explains how agents evolve beyond simple workflows into autonomous systems, why evals are at the heart of reliable AI, and how developers can prevent silent failures through better design, testing, and observability.You'll learn about:-Why most AI agents fail and how to engineer reliability from day one-Workflow agents vs LLM-based agents-How evals, memory hygiene, and adversarial testing improve reliability-When to use traditional ML instead of LLMs-Designing for human judgment, security, and recovery in agent systems#aipodcast #aiagents #aidevelopment #aiengineering #llm #mlops #datascience #agentdesign #workflowagents #memory #evaluation #productstrategy #aiproductmanagement #autonomousagents #aiethics #aideployments #reliableai #dataneighbor #jigyasagrover #agenticai

AI is reshaping data and analytics, moving from brittle dashboards to agentic, conversational workflows. In this Data Neighbor Podcast, we sit down with Barry McCardel, CEO & Co-founder of Hex, to unpack how agentic analytics, natural-language querying, and semantic modeling are changing how data teams (and the whole business) make decisions. Connect with Shane, Sravya, and Hai (tell us which platform sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Barry: https://www.linkedin.com/in/barrymccardel/In this episode, Barry shares how Hex evolved beyond notebooks into a self-serve BI + AI agent platform, why PMF is a moving target in AI, and how great data teams are shifting from ticket queues to curation, governance, and partnership.You'll learn about:- Agentic analytics in practice: from “chat with my data” to explainable, reproducible workflows (thinking traces, SQL visibility, versioned projects).- How semantic models (Hex, Snowflake, dbt, Cube) unlock trusted self-serve BI.- How to find PMF in AI: sustaining product-market fit when model capabilities shift weekly.- What is Data team 2.0: moving repetitive “pull a number” requests to agents so humans focus on curation, modeling, experimentation, and strategy.- How to ship rigor at speed: why transparency, lineage, and observability matter for trust—not just accuracy.#aiproductmanagement #agenticanalytics #conversationalbi #datateams #selfserveBI #semanticlayer #dbt #snowflake #dataapps #llm #aiagents #mlops #productstrategy #dataneighbor #hextech #hex #datascience #ai

Is the future of Machine Learning Engineer (MLE) jobs secure in the age of AI? Umang Chaudhary, an ML Engineer at TikTok (formerly Amazon), dives deep into this pressing question and shares his invaluable insights on navigating the rapidly evolving ML landscape. In this episode, Umang recounts his unique journey from web development to a thriving MLE career, the challenges of ML interview prep, and why he's now dedicated to guiding aspiring ML professionals.Discover how Umang leverages cutting-edge AI tools like Gemini and Grok in his daily workflow and for interview preparation, offering a fresh perspective on productivity and learning. Learn about the common fears and questions his mentees face regarding AI's impact on job security and how to differentiate between "real-world" ML skills and those needed to ace interviews. This episode is a must-watch for anyone looking to break into or advance in the ML field, offering a blend of career guidance, practical tips, and a compelling look into the future of AI.In this episode, you will learn:* The evolving role of AI and LLMs in daily ML workflows, from solution building to enhanced productivity.* How Umang leverages AI tools like Gemini Pro and Grok for efficient coding, document analysis, and comprehensive ML system design interview preparation.* Umang's unique journey, transitioning from web development to a Machine Learning Engineer role at Amazon and then TikTok.* Current concerns from aspiring ML professionals about AI's impact on the future of MLE jobs and Umang's perspective on career longevity.* Inspiring stories of individuals making unconventional transitions into ML engineering roles, including web developers, data analysts, and product managers.* A four-step plan to effectively break down and master Machine Learning interview preparation (ML fundamentals, ML design, ML system design, ML coding).* The critical importance of patience and a strategic "numbers game" approach to landing an ML job in today's competitive market.Connect with Umang:https://www.linkedin.com/in/mlwithumang/https://www.instagram.com/umangabroad/https://www.instagram.com/ml.with.umang/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#MLEngineer #MachineLearning #AIJobs #LLM #AICareers #MLCareerGuidance #MLInterviewPrep #TikTok #Amazon #DataScience #TechCareers #CareerTransition #Grok #ChatGPT #Gemini #Entrepreneurship #MachineLearningEngineer #AIInnovation #DataNeighborPodcast

Unlock the secrets to building a future-proof data organization that thrives on impact, not just effort. Join us as we sit down with Manoj Mohan, former Engineering Leader of Data and AI Platforms at Intuit, and a seasoned leader from Meta, Cloudera, and Apple. Manoj shares his deep insights from two decades in the data, ML, and AI space, offering pragmatic strategies for long-term success.In this episode, you’ll discover:- Hard-won lessons from early data warehouse failures and the critical role of humility and scalability in data projects.- Why embracing a "platform as a product" mindset for data engineering is essential for long-term efficiency and avoiding KPI chaos.- Manoj Mohan's powerful "3 Gs" framework (Grounded, Guarded, Governed) for deploying Large Language Models (LLMs) responsibly and effectively within the enterprise, comparing them to high-speed Formula One cars that need robust guardrails.- A visionary outlook on what a future-proof data organization might look like by 2030, where AI-driven insights are seamlessly accessible to every employee.- Practical advice for startups on balancing speed with sustainable data infrastructure, ensuring foundational blocks are built alongside product innovation.- Key principles for data leaders, including the importance of continuous learning, unlearning, and focusing on problem-solving over tools.Whether you're a data engineer, an AI enthusiast, a data leader, or navigating data challenges in a startup, this episode is packed with invaluable wisdom to help you build resilient, scalable, and impactful data systems.Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#DataEngineering #AIPlatforms #LLMs #DataStrategy #Scalability #DataGovernance #ResponsibleAI #PlatformAsAProduct #FutureOfData #DataOrganization #StartupData #EnterpriseAI #DataLeadership #MLEngineering #DataManagement #ManojMohan #DataNeighborPodcast #TechLeadership

AI is changing product management, from how teams prototype to how they measure success. In this episode of the Data Neighbor Podcast, we’re joined by Aman Khan, Head of Product at Arize AI (LLM evaluation & observability). Aman breaks down the three emerging AI PM archetypes (AI-native PM, AI platform PM, and AI-powered PM), how to move from “vibe coding” to eval-driven development (EDD), and why aligning evals to business outcomes matters more than any single accuracy score. He also shares hard-won tactics for handling subjectivity in LLM outputs, setting user expectations in UX, and deciding when rigor can (and can’t) slow down speed. In this episode, you’ll learn:-The three ways AI shows up in PM work—and how those roles are converging.-A practical ladder from “vibe checks” to EDD (evals in dev & production), including LLM-as-a-judge and when to trust it.-How to tie evals to business metrics (trust, value, speed) and resolve “good eval, bad outcome” conflicts.-UX patterns for long-running agent tasks (progress, ETAs, checkpoints) that preserve trust.-Where AI coding tools help most (and least) across engineers, PMs, and data teams.Connect with Aman Khan:LinkedIn: https://www.linkedin.com/in/amanberkeley/🌐 Website: https://amank.ai🏢 Arize AI: https://arize.com/ Arize AIConnect with Shane, Sravya, and Hai (let us know which platform sent you!):👉 Shane Butler: https://linkedin.openinapp.co/b02fe👉 Sravya Madipalli: https://linkedin.openinapp.co/9be8c👉 Hai Guan: https://linkedin.openinapp.co/4qi1r#aiproductmanagement #aievals #llmobservability #productmanagement #datascience #mlops #aiagents #evaluation #productstrategy #dataneighbor #arizeai #llms

Are you overwhelmed by ad-hoc data questions? Ever wondered how to automate business intelligence with AI? Join us as we sit down with Lohitaksh Yogi, a seasoned AI product leader from companies like ServiceNow and Adobe, to explore the exciting world of AI data agents and Natural Language Business Intelligence (NLBI). Lohit shares his journey from early machine learning chatbots to cutting-edge LLM-powered conversational AI, offering invaluable insights into building and deploying these transformative systems.In this episode, you will learn:- The evolution of chatbots: Understanding the limitations of early rule-based systems vs. the powerful context-awareness of LLMs.- The vision for Natural Language Business Intelligence (NLBI): How close we are to asking a chatbot natural language questions and getting instant insights from our data.- Key challenges in AI deployment: Navigating schema ambiguities, data inconsistencies, and the critical issue of AI hallucinations.- Strategies for building an effective AI data agent: From designing intuitive user experiences (UX) to implementing robust error handling and feedback loops.- The paramount importance of data governance: Protecting sensitive information and ensuring data privacy when leveraging AI for internal data analysis.- Why internal beta testing is crucial: Breaking your system internally before exposing it to external stakeholders to build trust and ensure accuracy.- The right mindset for AI adoption: Viewing AI as an investment for long-term productivity gains, not a quick fix, and understanding its rapid evolution.Whether you're a data professional looking to boost productivity, a business leader seeking to automate data requests, or just curious about the future of AI in the enterprise, this episode provides actionable strategies and a realistic outlook on deploying AI data agents.Connect with Lohitaksh: https://www.linkedin.com/in/lohitakshyogi/Connect with Hai, Sravya, and Shane (let us know YouTube sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#Chatbots #ConversationalAI #AIDataAgents #NaturalLanguageBI #LLM #LargeLanguageModels #DataGovernance #AIHallucination #DataAnalytics #DataScience #MachineLearning #ProductLeadership #AIStrategy #UserExperience #DataWorkflow #AIPodcast #TechInsights #DataProduct #EnterpriseAI

What truly defines a good data scientist, and how can you excel in this rapidly evolving field? Join us as we sit down with Siddharth Ranganathan, Director of Data Science at Microsoft, to uncover practical insights on navigating data science careers, balancing rigor with business needs, and the transformative impact of AI. Siddharth shares invaluable lessons from his extensive experience, emphasizing impact over complexity and strategy over execution.In this episode, we cover:- What constitutes good data science: Focusing on decisions, impact, scientific rigor, and practicality.- Balancing speed and rigor in analysis: Strategies for delivering timely insights without compromising integrity.- Common misunderstandings about product data science: It's more than just building ML models; it's a strategic, cross-functional role.- How to become a strategic data scientist: Shifting focus from outputs to outcomes and asking better questions.- The evolving landscape of data science with AI and Gen AI: Anticipating the rise of role-based agents and the convergence of tech and business.- Identifying and avoiding common career traps for data scientists, such as staying in execution mode or over-indexing on technical depth.- Key factors directors look for in promotions: Driving impact beyond your current level, securing patrons, and clearly communicating your contributions.- The most underrated skill for a data scientist: The ability to break down complex problems and deal with ambiguity.Whether you're an aspiring data scientist, a mid-level professional looking to grow, or a leader shaping data teams, this episode offers a wealth of actionable advice to elevate your data science career and impact.Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#DataScience #ProductDataScience #AI #GenAI #LLMs #CareerGrowth #StrategicDataScientist #Microsoft #DataScienceCareer #DataSciencePromotions #DataScienceAdvice #DataScienceLeadership #ImpactOverComplexity #TradeOffs #DataNeighborPodcast

AI is fundamentally changing how we build and manage products—but agentic AI takes things to an entirely new level. In this episode of the Data Neighbor Podcast, we’re joined by Mahesh Yadav, who has built and launched AI-driven products at leading FAANG companies including Microsoft, Meta, Amazon, and Google. He’s also the creator of the popular Maven course on Agentic AI Product Management: https://maven.com/mahesh-yadav/genaipmMahesh shares firsthand insights into how the product lifecycle for AI-driven features differs from traditional development, the critical importance of robust evaluations, and how teams can practically adapt to the rapidly evolving landscape of AI. Whether you're a product manager, data scientist, engineer, or executive navigating the complexities of integrating AI into your products, this episode is your practical guide to thriving in the AI-first world.In this episode, you'll learn:- How the product lifecycle for agentic AI products differs from traditional software.- Practical frameworks for effectively evaluating AI performance and quality.- The role of subject matter experts and evaluation scientists in scaling AI products.- Strategies for staying ahead as AI reshapes traditional roles and team structures.Connect with Mahesh Yadav:🔗 LinkedIn: https://www.linkedin.com/in/initmahesh/🎓 Maven Course on Agentic AI Product Management: https://maven.com/mahesh-yadav/genaipmConnect with Shane, Sravya, and Hai (let us know which platform sent you!):👉 Shane Butler: https://linkedin.openinapp.co/b02fe👉 Sravya Madipalli: https://linkedin.openinapp.co/9be8c👉 Hai Guan: https://linkedin.openinapp.co/4qi1r#ai #agenticai #productmanagement #productdevelopment #llms #aiproductmanagement #aiagents #aieval #evaluationmetrics #machinelearning #datascience #faang #productstrategy #dataanalytics #dataneighbor #businessintelligence #productleadership

Ercan Kamber, former Chief Data Officer at Angi and seasoned leader from Twitter and Microsoft, joins the Data Neighbor Podcast for a masterclass on scaling data organizations, embracing AI, and navigating C-suite challenges. As the first CXO to appear on the show, Ercan opens up about what it really means to be a CDO, the mindset shift from tech contributor to enterprise-wide leader, and how to build AI-empowered data teams that matter.In this episode, we cover:🏗️ How Ercan built Angi’s first centralized data org after multiple mergers.📈 The real meaning of “data strategy” in complex business environments.🧭 Transitioning from big tech to startup C-suite: lessons in ownership and context switching.🧠 The rise of AI agents: What AI-first and AI-forward really mean - and why it matters.⚖️ Balancing speed, cost, and precision in ML systems.📊 How to create a scalable operating system for modern data teams using Agile.👁️ Communication secrets for working with executive teams.💡 What the future of AI agents might mean for labor, startups, and society.This episode is packed with hard-earned wisdom and actionable advice, whether you’re a rising data scientist or leading data for a global enterprise. Ercan brings both vision and pragmatism - don’t miss this conversation!Connect with Hai, Sravya, and Shane:Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya: https://www.linkedin.com/in/sravyamadipalli/Shane: https://www.linkedin.com/in/shaneausleybutler/#datascience #aiagents #chiefdataofficer #dataleadership #cdorole #bigtechcareer #dataorganization #mlops #datateams #aifuture #agenticai #datastrategy #dataneighborpodcast #aiinbusiness #cdoinsights

Your data insights are worthless if no one understands them. In this episode of the Data Neighbor Podcast, we’re joined by Matt Harrison, author of Effective Pandas, Effective Visualization, and many more bestselling technical books. Matt joins us to uncover the secrets behind impactful, professional data storytelling.Learn how to transform complex data into clear, compelling narratives that resonate with stakeholders and drive action. Whether you're a data scientist, analyst, product manager, or anyone who deals with data visualization, Matt’s proven 5-step CLEAR framework will help you craft visuals that communicate with clarity, simplicity, and effectiveness.In this episode, you'll learn:* How to avoid common mistakes data professionals make when visualizing data.* Why "fancy" charts often fail and how to master simple visuals that tell better stories.* Practical tips for using color, annotations, and design principles like a pro.* How top media outlets (New York Times, The Economist) use these exact methods to captivate their audiences.Connect with Matt Harrison:📚 Website: https://www.metasnake.com🔗 LinkedIn: https://www.linkedin.com/in/panelaConnect with Shane, Sravya, and Hai (let us know YouTube sent you!):👉 Shane Butler: https://linkedin.openinapp.co/b02fe👉 Sravya Madipalli: https://linkedin.openinapp.co/9be8c👉 Hai Guan: https://linkedin.openinapp.co/4qi1r#datastorytelling #datavisualization #datascience #analytics #python #matplotlib #effectivevisualization #pandas #storytellingwithdata #visualcommunication #machinelearning #datastrategy #dataskills #dataneighbor #dataanalytics #datascientist #dataengineering #businessintelligence

Ever wondered how someone with a political science degree ends up doing machine learning at Twitch? Meet Marina Wyss - applied scientist, blogger, YouTuber, and all-around productivity and learning expert. In this episode of the Data Neighbor Podcast, Marina shares her unconventional journey into tech, how she self-taught herself machine learning, and why her mantra of being “gratitude-driven” is her antidote to hustle culture.We dive into:- How Marina transitioned from political science and jewelry management into data science and ML.- Her self-study roadmap: From free courses to Coursera to deep technical books.- Practical frameworks for self-learning, getting promotions, and breaking into the ML industry.- Why she rejects hustle culture in favor of a gratitude-driven approach to productivity.- How she leverages AI tools like ChatGPT and Replit to accelerate learning and personal projects.- Common mistakes early learners make and how to avoid being overwhelmed.- Her take on Python vs R, what to focus on when starting ML, and why building your own projects is essential.Whether you're coming from a non-technical background, looking to break into ML, or trying to navigate learning in the age of AI, Marina’s story and advice will inspire you to take the leap - and build the skills that matter.Links Mentioned in the EpisodeMarina’s Blog: https://www.gratitudedriven.com/Books Mentioned:- Designing Machine Learning Systems by Chip Huyen- AI Engineering by Chip Huyen- Software Engineering for Data Scientists by Catherine NelsonCourses:- Machine Learning Specialization by Andrew Ng (Coursera)- Deep Learning Specialization (deeplearning.ai)- Math for Machine Learning (Three Blue One Brown on YouTube)Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya: https://www.linkedin.com/in/sravyamadipalli/Shane: https://www.linkedin.com/in/shaneausleybutler/#machinelearning #datascience #careertransition #gratitudedriven #selfstudy #ai #deeplearning #python #coursera #careerroadmap #productivity #chatgpt #learnML #dataeducation #DataNeighborPodcast #nontechtotext #womenintech #mlprojects

AI systems are becoming integral to nearly every digital product, but their vulnerabilities pose real and serious risks. How can companies protect their AI-powered products from security threats like prompt injection, jailbreaking, and misalignment?In this episode of the Data Neighbor Podcast, we're joined by Sander Schulhoff, CEO of Hacker Prompt and founder of Learn Prompting, to uncover critical security flaws in AI systems and practical ways to defend against them. With insights gathered from over 600,000 real-world AI exploits, Sander breaks down the three most dangerous AI security failures threatening today's products and provides actionable strategies to safeguard your systems.Connect with Sander Schulhoff:LinkedIn: https://www.linkedin.com/in/sander-schulhoff/AI Red Teaming Masterclass: https://maven.com/learn-prompting-company/ai-red-teaming-and-ai-safety-masterclassHack A Prompt: https://www.hackaprompt.com/Learn Prompting: https://learnprompting.org/Connect with Shane, Sravya, and Hai (let us know YouTube sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rYou'll learn essential techniques for securing AI systems, including how to recognize and prevent prompt injection and jailbreaking attacks, strategies for detecting misalignment early, and how to effectively leverage automated red teaming alongside human expertise. Sander also explores why security considerations must move from late-stage fixes to foundational aspects of AI model development and deployment.We discuss emerging security threats with autonomous agents, the role of government and compliance in AI security, and practical advice for teams at any stage—from startups to large enterprises—to proactively address AI security.If you're a data scientist, product leader, security professional, or executive interested in deploying secure AI systems, this episode provides critical insights and practical steps to protect your products and your users.#AIsecurity #promptinjection #jailbreaking #redteaming #aisafety #machinelearningsecurity #aiattacks #dataprotection #aivulnerabilities #automatedredteaming #agenticAI #hackaprompt #aiethics #dataneighbor

Generative AI applications are transforming industries, but taking a GenAI model from prototype to production can be challenging. How can teams effectively build, evaluate, and deploy powerful generative AI systems in real-world scenarios? In this episode of the Data Neighbor Podcast, we're joined by Surabhi Bhargava, a Machine Learning Tech Lead at Adobe, to explore the step-by-step process of creating and productionizing GenAI apps, including embedding strategies, chunking techniques, retrieval-augmented generation (RAG), prompt engineering, and advanced model evaluation.In this episode, you'll learn essential insights into how to build a GenAI app, including how to select the right embeddings and chunk size, effective vector database management, and methods for robust query reformulation. Discover best practices for integrating GPT, Claude, Azure AI, and OpenAI APIs into your machine learning pipelines. Surabhi also shares critical tips on optimizing your AI prototype for user testing, identifying the ideal tech stack, and managing iterative feedback.We explore how to properly evaluate GenAI models using automated and human-in-the-loop strategies, discuss practical metrics to measure AI accuracy and performance, and reveal common pitfalls in AI application development. You'll also gain insights into personalization, user experience considerations, resource management, and understanding when not to use LLMs.If you’re a data scientist, engineer, product manager, or executive looking to deepen your understanding of generative AI and effectively move AI projects from concept to production, this episode is your ultimate guide.Connect with Surabhi Bhargava:LinkedIn: https://www.linkedin.com/in/surabhibhargava/Connect with Shane, Sravya, and Hai (let us know which platform sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1r

From Data Scientist to Product Manager to Software Engineer - all within Airbnb. In this episode of the Data Neighbor Podcast, we dive deep into Robert Chang’s remarkable trifecta career journey. If you've ever wondered how to pivot roles in tech, build social capital, or thrive across vastly different domains, this conversation is a must-watch.Robert shares:- His full-circle 10-year journey through data, product, and engineering.- Behind-the-scenes insights on building and scaling Airbnb’s metrics infrastructure.- The chaos and lessons from jumping into a PM role during a system-wide incident.- Why closing the loop and building with impact matters more than sticking to rigid job descriptions.- The real differences between maker vs. manager schedules - and how to find your fit.- The unexpected power of cross-functional empathy in building better teams and products.- How to embed learning into your actual work so it sticks and scales.- And of course… what “Hard Mode” really feels like (and why he chooses it, over and over again).Whether you're early in your career, thinking of switching roles, or simply want a raw, thoughtful perspective on navigating tech from within, Robert’s story will resonate. He doesn’t just talk about pivoting - he’s done it, with clarity, humility, and strategic intent.Connect with Hai, Sravya, and Shane (let us know YouTube sent you!):Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya: https://www.linkedin.com/in/sravyamadipalli/Shane: https://www.linkedin.com/in/shaneausleybutler/#DataNeighborPodcast #RobertChang #AirbnbTech #CareerPivot #TechCareers #DataScience #ProductManagement #SoftwareEngineering #InfraAtScale #MetricsPlatform #TechTransitions #PMLife #DataInfra #CareerGrowthInTech #CrossFunctionalSkills #ClosingTheLoop #GenAI #HardMode

AI is everywhere, but Responsible AI and AI ethics have never mattered more. How can teams ensure they build trustworthy AI systems free of dangerous AI bias and problematic algorithmic bias? In this episode of the Data Neighbor Podcast, we're joined by Will Landecker, founder of Accountable Algorithm, to dive deep into AI safety, ethical AI, and best practices for responsible development.This episode explores foundational topics, including an introduction to Responsible AI, core Responsible AI principles, and practical strategies for addressing LLM hallucination, evaluating LLMs, and managing human-AI interaction. Learn what AI transparency, AI accountability, and AI fairness really mean, and how they impact your business and users.Will shares actionable insights on bias mitigation in AI, building fair AI models, and achieving AI explainability. We'll uncover the hidden AI blind spots that damage trust, discuss essential AI governance methods, and reveal tactics for effective AI risk management. From ethical machine learning frameworks to the latest strategies in detecting and preventing AI discrimination, this episode covers everything you need for developing truly ethical products.If you're a data scientist, product manager, engineer, or executive interested in the risks and rewards of AI, this is your essential guide to the complex intersection of AI and society. Discover how responsible practices aren't just good ethics—they're good business.Connect with Will Landecker:Website: https://accountablealgorithm.com/LinkedIn: https://www.linkedin.com/in/will-landecker-88b2a788/Connect with Shane, Sravya, and Hai (let us know which platform sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1r#ai #responsibleai #aibias #aigovernance #aiethics #ethicalai #algorithmicbias #llmhallucination #aifairness #aisafety #aitransparency #aiexplainability #ethicalmachinelearning #biasmitigation #aiaccountability #aiandsociety #humanaiinteraction #trustworthyai #aiblindspots #risksai #machinelearning #aialignment #dataneighbor

Ever wondered why you might know more math, physics, or logic than you realize - and why traditional education often makes learning feel way harder than it should? In this mind-expanding episode, we sit down with Luis Serrano, founder of Serrano Academy to break down how real learning happens, why AI won’t replace the most human parts of us, and how quantum computing might just change the future of everything.In this conversation, Luis shares: - How struggling with math as a kid led him to a PhD and global teaching success. - Why "playing" is the best way to learn technical subjects like machine learning - not memorizing formulas. - The true power of intuition, emotional intelligence, and creativity in an AI-dominated world. - Quantum computing explained simply: why qubits, entanglement, and "magic" matter. - Actionable advice on how to learn faster, get past fear of complexity, and build irreplaceable skills for the future.Whether you're starting your journey in machine learning, curious about quantum computing, or wondering how to stay relevant in an AI-powered world, this episode is packed with practical inspiration and futuristic insights you don't want to miss. Watch till the end - trust us, it’s worth it!Serrano Academy: https://www.youtube.com/@SerranoAcademyConnect with Hai, Sravya, and Shane (let us know which platform sent you!): Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/ Sravya: https://www.linkedin.com/in/sravyamadipalli/ Shane: https://www.linkedin.com/in/shaneausleybutler/

What does it really take to be the first data hire at a startup?In this episode of the Data Neighbor Podcast, we dive deep with Jessie Li - former Head of Data at Descript and ex-Meta product data science leader - as she unpacks her raw, insightful journey navigating the unpredictable world of early-stage startups. From leaving Big Tech for a less-known startup to building a full-stack data function from scratch, Jessie shares the highs, lows, and the very real lessons learned along the way.We cover: - What shocked Jessie most on Day 1 as Descript’s first data hire. - Why bringing Big Tech best practices into a startup doesn’t always work. - How to build data foundations, insights functions, and culture from zero. - The push/pull model for career transitions (and how to think two jobs ahead). - Why being “too humble” in a startup can backfire - and how to avoid disorientation. - What startups really need in data hires - and how to know if you're a good fit. - The underrated importance of AI in data workflows today.Whether you're contemplating a startup move or want an honest look at what it takes to own and scale data in a high-growth environment, this is an unfiltered and inspiring conversation packed with lessons for data professionals at every level.Connect with Jessie: https://www.linkedin.com/in/jessieljy/Connect with Hai, Sravya, and Shane (let us know YouTube sent you!):Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya: https://www.linkedin.com/in/sravyamadipalli/Shane: https://www.linkedin.com/in/shaneausleybutler/#DataScience #Startups #JessieLi #FirstDataHire #Descript #DataCulture #DataEngineering #AIInData #CareerGrowth #BigTechVsStartup #DataNeighborPodcast #DataLeadership #IntentionalLearning #StartupHiring #WomenInData #DataDriven

In this episode of Data Neighbor, we dive deep into the rapidly evolving landscape of education driven by AI. Jess Haberman, Director of Product Content at Anaconda and former O'Reilly editor, shares groundbreaking insights on how generative AI tools are revolutionizing the way we learn Python, data science, data engineering, and beyond - making learning faster and more accessible than ever before.​ AI is even teaching us how to speak with ai, how to build ai, and how to learn how ai works.Key Takeaways:-How AI enables learners to master Python and technical skills significantly faster.-The future of traditional education, online courses, and tech bootcamps.-Critical soft skills you need to thrive in the AI-powered job market.-Why AI-assisted learning is now a necessity, not just an advantage.-Practical strategies to leverage generative AI for your professional development.​Connect with Jess:LinkedIn: https://www.linkedin.com/in/colinmatthews-pm/X: https://x.com/JessHabermanBluesky: https://bsky.app/profile/jesshaberman.bsky.socialWebsite: https://www.jesslhaberman.com/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Shane Butler: https://linkedin.openinapp.co/b02feHai Guan: https://linkedin.openinapp.co/4qi1rSravya Madipalli: https://linkedin.openinapp.co/9be8c

Ever wondered who’s behind the ads that actually make you buy stuff—and how companies waste millions on the ones that don’t? In this mind-opening episode of the Data Neighbor Podcast, we dive into the world of marketing science with Agastya Komarraju, Global Head of Product and Science for Growth & Funnel at Amazon. Agastya has held leadership roles at Walmart and Nielsen, and he’s here to demystify how data powers smarter marketing—and how Amazon slashed 90% of marketing spend without hurting results.- What exactly is marketing science and how does it differ from traditional data science? - Why “50% of your marketing spend is wasted”—and how to figure out which half. - How direct mail still outperforms digital ads (yes, seriously). - The five pillars of marketing science—from experimentation to attribution. - How AI and GenAI are transforming marketing from creative to targeting. - Real-world wins: The genius experiments that won Agastya and his teams top industry awards.Whether you're a marketing leader, data scientist, or just someone who wants to understand why you keep seeing that ad for a coffee mug you Googled once, this episode is packed with insights on consumer behavior, optimization, and the science behind persuasion. Learn how the biggest brands in the world are applying data—and what that means for your role, your budget, and your career.Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Agastya: https://www.linkedin.com/in/agastya-kumar-komarraju-95b60446/Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya: https://www.linkedin.com/in/sravyamadipalli/Shane: https://www.linkedin.com/in/shaneausleybutler/

How is AI quietly reshaping your everyday workflow—whether you notice it or not? In this episode of the Data Neighbor Podcast, we explore how AI is being embedded directly into the tools you already use, with real-world examples from Adobe Acrobat.Our guest, Nikhil Pentapalli, is a Senior Machine Learning Engineer at Adobe, where he works on embedding GenAI into Acrobat to transform how people interact with documents. From AI assistants that can summarize and answer questions across multiple PDFs, to intelligent restructuring of scanned documents for mobile, we unpack what it really looks like to bring AI into production—and why it’s harder than it seems.We cover:- How AI is becoming invisible infrastructure in everyday workflows. - Why integrating AI into documents is harder than you'd think. - What makes PDFs complex, and how GenAI is making them smarter. - Building GenAI products: from prompt engineering to memory optimization. - Advice for breaking into GenAI, and what ML engineers need to know now. - Traditional ML vs LLMs: where to use which, and how to manage costs.Whether you're building AI tools, thinking about career moves, or just curious about what it takes to ship real GenAI products, this episode is loaded with insights and practical advice.Connect with Hai, Sravya, and Shane:- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#genai #aiworkflow #documentai #rag #retrievalaugmentedgeneration #acrobatai #llm #promptengineering #mlengineer #aiintegration #realworldai #aiproducts #datascience #dataneighborpodcast

AI is transforming how products are built—and with new AI prototyping tools like Replit, Cursor, V0, Bolt, and Lovable, it’s never been easier to go from idea to working app in minutes. In this episode of the Data Neighbor Podcast, we’re joined by Colin Matthews, founder of Tech for Product, to explore how product managers, data scientists, and engineers can build faster using AI.Colin teaches two of the top AI and technical fluency courses on Maven, writes the Tech for Product Substack, and helps builders leverage tools like Replit and GPT to build and test real products—fast.📚 Courses from Colin:AI Prototyping for Product Managers: https://maven.com/tech-for-product/ai-prototyping-for-product-managersTechnical Foundations for Product Managers: https://maven.com/tech-for-product/tech-fundamentals💼 Follow Colin Matthews:LinkedIn: https://www.linkedin.com/in/colinmatthews-pm/Substack: https://blog.techforproduct.com/Website: https://techforproduct.com/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai Guan: https://linkedin.openinapp.co/4qi1rSravya Madipalli: https://linkedin.openinapp.co/9be8cShane Butler: https://linkedin.openinapp.co/b02feIn this episode, we cover:- How to prototype real apps with AI (Replit, Cursor, Lovable, Bolt, V0)- How AI is changing the role of product managers and engineers- Replit demo: How we built a working data analysis app with zero code- AI vs traditional product development: What’s faster?- When these tools are actually production-ready- How product teams can use AI to validate ideas early- AI tools for data science, analytics, and product strategy- Best practices for debugging and building with AI agents- The real role of product-market fit in AI-first prototyping- How to avoid common mistakes when using AI tools- Colin’s advice for staying up to date with the fast-moving AI stackIf you work in AI, product, data science, machine learning, or tech strategy—or you’re a founder trying to get your idea off the ground—this episode is a goldmine of practical insights. From MVPs to experimentation platforms, we explore how AI is changing who gets to build, how fast they can do it, and what the future of product development looks like.#AI #AIPrototyping #ProductManagement #ProductDevelopment #DataScience #MachineLearning #AIApps #Replit #Cursor #V0 #Bolt #Lovable #AIForStartups #NoCodeAI #GPTApps #TechForProduct #MavenCourses #AICodingTools #BuildWithAI #AIProductStrategy #AIforPMs #RapidPrototyping #TechnicalFluency #LLMTools #AIStack #AIEngineering #DataTools #AIUX #Streamlit #OpenAI #AIAppDemo

Are you struggling to land a data science role in today’s competitive market? In this episode, we sit down with Karun Thankachan, Senior Data Scientist at Walmart and former Amazon Applied Scientist, to break down the science of job searching - including actionable strategies to optimize your job applications, referrals, and LinkedIn outreach. Oh and we covered how recommendation systems are built and what all go into them in practice.What You’ll Learn in This Episode:- The metrics-driven approach to job searching: How many applications, referrals, and LinkedIn connections should you aim for?- How AI is changing hiring for both recruiters and candidates- The importance of networking and why it’s the most underrated skill in data science- A deep dive into recommender systems—how AI powers Netflix, Amazon, and Walmart recommendations- How LLMs (Large Language Models) are revolutionizing recommendation enginesIf you’re an aspiring data scientist, a mid-career professional looking for new opportunities, or just interested in how AI is reshaping hiring and recommender systems, this episode is packed with insights you don’t want to miss!Connect with Karun, Hai, Sravya, and Shane:Karun: https://www.linkedin.com/in/karunt/Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02fe#DataScience #JobSearch #AIHiring #RecommenderSystems #MachineLearning #NetworkingTips #CareerGrowth #AI #Walmart #Amazon #DataNeighborPodcast #TechHiring

AI is evolving faster than ever—and open-source AI models are catching up to proprietary models at an incredible pace. In this episode of the Data Neighbor Podcast, we sit down with Maarten Grootendorst, co-author of Hands-On Large Language Models with Jay Alammar, DeepLearning.AI instructor, and creator of BERTopic and KeyBERT, to break down the real differences between open-source and closed-source AI models.We’ll discuss how LLMs (Large Language Models) evolved from bag-of-words and Word2Vec to modern transformer-based models like BERT, GPT-4, DeepSeek, LLaMA 2, and Mixtral. More importantly, we explore when open-source AI models might actually be better than proprietary models from OpenAI, Google DeepMind, and Anthropic.Hands-On Large Language Models (Maarten’s Book): https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961DeepLearning.AI Course: How Transformer LLMs Work: https://www.deeplearning.ai/short-courses/how-transformer-llms-work/Maarten’s AI Newsletter: https://newsletter.maartengrootendorst.com/Connect with us!Maarten Grootendorst: https://www.linkedin.com/in/mgrootendorst/Hai Guan: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya Madipalli: https://www.linkedin.com/in/sravyamadipalli/Shane Butler: https://www.linkedin.com/in/shaneausleybutler/

In this episode of the Data Neighbor Podcast, we sit down with Justin Chen, Senior Director of Growth Data Science and former Head of Data Science & Engineering at Coinbase, to dive deep into how to build scalable, high-impact data teams. We get into the mind of a data leader who has built numerous reputable data organizations to understand principles, lessons, and challenges every company faces. Justin shares invaluable insights on:- Go slow to go fast – Why early-stage speed can create long-term inefficiencies.- The evolution of data science – How different orgs (growth, core, platform) function within companies.- The myth of the unicorn data scientist – Why hiring for specialization is key.- AI's impact on data science – How automation and AI tools are shaping the future of analytics.- Getting a seat at the table – How data professionals can move from support roles to strategic leadership.Justin also shares his firsthand experience of building an AI-powered data copilot at Coinbase to streamline analytics workflows, offering a sneak peek into how AI will shape the next generation of data teams. Whether you’re a data scientist, engineer, or aspiring leader, this conversation is packed with practical advice and industry wisdom you won’t want to miss!Connect with Justin: https://www.linkedin.com/in/mingc/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02fe#DataScience #AI #aiengineering #TechLeadership #MachineLearning #GrowthDataScience #CareerAdvice #Analytics #Hiring #DataEngineering #DataDriven #DataTeams #TechPodcast #aiagents

AI is changing everything—including how we moderate content online. In this episode of the Data Neighbor Podcast, we sit down with Sugin Lou, a Staff Data Scientist at Cash App and former Nextdoor AI trust & safety expert, to discuss the challenges of AI content moderation, misinformation, and trust & safety in the era of LLMs.If you care about AI trust, AI policy, risk of AI, and AI governance, this episode is for you.More about this episode:What is content moderation? How does AI impact trust & safety? From Facebook moderation to moderation bots and comment moderation, companies rely on AI-powered moderation tools to detect AI-generated content, deepfakes, misinformation, and harmful speech. But is AI moderation really working, or is it just scaling misinformation at an unprecedented rate?AI Misinformation & Risk Management:With AI-generated content, fake AI identities, and deepfakes spreading faster than ever, AI-powered disinformation is becoming a serious issue. We explore how AI risk management, AI governance, and AI regulation are trying to catch up before AI trust is lost forever.Trust & Safety in AI:How do platforms like Facebook, YouTube, and Nextdoor determine what content gets removed? How does the moderation process work? And what are the hidden risks of AI trust & safety failures?Evaluating AI Models for Trust & Safety:How do companies evaluate LLMs and ensure AI-generated content isn’t spreading misinformation? We discuss the latest in AI safety, LLM evaluation, and how companies like OpenAI, Google, and Anthropic are handling AI fraud, AI accountability, and AI disinformation.Key Topics Covered:-What is content moderation? AI’s role in trust & safety-AI moderation bots vs. human moderation-Facebook moderation & the future of AI content filtering-The hidden risks of AI-generated content & deepfakes-How AI is breaking the internet—for better or worse-AI misinformation detection & AI disinformation at scale-AI fraud, risk assessment, and AI accountability-How AI safety teams are responding to AI threats-With AI moderation tools, chat moderation, and content filtering AI, tech companies are trying to prevent AI-powered misinformation while balancing --AI ethics, AI regulation, and free speech. But can AI content moderation actually keep up?Connect with us!Sugin Lou: https://www.linkedin.com/in/sugin-lou/ Hai Guan: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya Madipalli: https://www.linkedin.com/in/sravyamadipalli/Shane Butler: https://www.linkedin.com/in/shaneausleybutler/#AI #ArtificialIntelligence #MachineLearning #AIContentModeration #FacebookModeration #ModerationBot #AITrust #AIAccountability #AIMisinformation #Deepfake #AIRegulation #TrustAndSafety #GenerativeAI #LLMEvaluation #AITrustAndSafety #AICompanions #AIEthics #AIModerationTools #WhatIsContentModeration

In this episode of the Data Neighbor Podcast, discover how data science shapes both winning and losing productr. We explore key insights from staff data science expert Anagh Pal with experience at Amazon, Twitter, and Nextdoor. Learn how cross-functional collaboration, data-driven experimentation, and hypothesis testing drive product success and avoid common pitfalls. Whether you're a data scientist, product manager, or just curious about how tech products evolve, this episode reveals essential strategies and lessons.Understanding product analytics and product development is crucial for anyone in data science, product management, or product design. In this episode, we dive into the product roadmap process and how data scientists and product managers collaborate to drive innovation. Learn how data analytics roadmaps influence decision-making, A/B testing, and experimentation to build products that users love. Whether you're wondering "What is data science?", planning your data science roadmap, or looking to advance in product management, this episode provides insights into real-world applications at top tech companies. Discover the key metrics, frameworks, and cross-functional strategies used by leading product managers and data scientists to launch, refine, and scale successful tech products.Connect with Hai, Sravya, Shane, and Anagh (let us know YouTube sent you!):Hai Guan: https://linkedin.openinapp.co/4qi1rSravya Madipalli: https://linkedin.openinapp.co/9be8cShane Butler: https://linkedin.openinapp.co/b02feAnagh Pal: https://www.linkedin.com/in/anaghpal/Learn about:-The role of data science in product success and failure-How data collaboration with PMs and engineers shapes outcomes-Data experimentation, A/B testing, and analyzing key metrics-Earning trust as a data scientist in cross-functional teams-Insights from careers at Amazon, Twitter, and Nextdoor-The future of data science and product development in 2025#datascience #productdevelopment #abtesting #collaboration #techroadmap2025 #dataanalystroadmap #productroadmap #datastrategies #datascientistrole #bigtech

AI is evolving faster than ever, and DeepSeek has sent shockwaves through the industry. In this episode of the Data Neighbor Podcast, we sit down with Dr. Raffaele Ciriello, a scholar in information systems at the University of Sydney, to unpack the impact of DeepSeek and its implications for the future of AI innovation, sustainability, and ethics.DeepSeek R1 and DeepSeek V3 have positioned themselves as major competitors to OpenAI's GPT-4, Google's Gemini, and Meta's LLaMA models—but what makes them different? In this episode, we discuss how DeepSeek AI is redefining the large language model (LLM) space, how its open-source AI model challenges proprietary AI, and whether it could be the best free AI alternative. We also explore the financial impact on Nvidia stock, as DeepSeek’s approach proves that cutting-edge AI doesn’t require billions in GPU investment. Plus, we break down how DeepSeek's mixture-of-experts (MoE) architecture is reshaping AI efficiency, its potential risks in AI companionship and generative AI, and what this means for the future of AI regulation and governance.🔗 Links Mentioned:Connect with Raffaele: https://www.linkedin.com/in/raffaele-ciriello/Raffaele’s Google Scholar: https://scholar.google.ch/citations?user=BIJHqJYAAAAJ&hl=enRaffaele’s ResearchGate: https://www.researchgate.net/profile/Raffaele-CirielloLatest article from Raffaele on OpenAI's Deep Research capability: https://theconversation.com/openais-new-deep-research-agent-is-still-just-a-fallible-tool-not-a-human-level-expert-249496Raffaele's article on Compassionate AI Design, Governance, and Use: https://www.techrxiv.org/users/886325/articles/1264666-compassionate-ai-design-governance-and-use🔍 Key Topics Covered:🔥 The rise of DeepSeek and why it’s a game-changer💰 How DeepSeek shattered AI’s cost barriers🆚 Open Source vs. Proprietary AI: What’s at stake?⚡ The environmental impact of AI & the Jevons Paradox🚀 AI regulation, safety, and the road aheadWe also dive into AI companionship, the ethical risks of unchecked AI, and how open-source AI could empower more responsible governance. Don’t miss this eye-opening discussion!Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02feIf you need personalized advice for your data science career roadmap, send us a message on LinkedIn or post a comment below!

AI is creating a massive career arbitrage moment—but are you taking advantage of it? In this episode, we sit down with Yuzheng, an AI course instructor, ex-Meta, Amazon, Tencent, and a leading voice in the GenAI space. Yuzheng breaks down how the next 10-18 months will redefine careers and why those who embrace AI now will skyrocket ahead.🔹 AI isn’t replacing jobs—it’s amplifying potential.🔹 The skills that matter in the GenAI world (and what’s becoming obsolete).🔹 How AI-powered tools can make you a force multiplier at work.🔹 The mindset shift needed to stay ahead of the AI curve.Whether you're a data scientist, analyst, machine learning engineer, or just AI-curious, this episode will give you a practical roadmap to stay ahead before this window of opportunity closes.🔗 Links Mentioned in the Episode👉 Yu Zheng's AI Course on Maven: https://maven.com/kedaibiao/genai👉 Cursor: https://www.cursor.com/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai: https://linkedin.openinapp.co/4qi1rSravya: https://linkedin.openinapp.co/9be8cShane: https://linkedin.openinapp.co/b02feIf you need personalized advice for your data science career roadmap, send us a message on LinkedIn or post a comment below!

Ever wondered how to make the complex simple and the technical engaging? Join us for a fascinating episode as we sit down with Josh Starmer, the mastermind behind the wildly popular YouTube channel  @statquest ! Known for making statistics and machine learning concepts approachable, Josh shares his secret sauce to storytelling, simplifying concepts, and tips and tricks to help one become better at communication in general. In this episode, we cover: 🌟 The origin story of StatQuest: From a genetics lab to YouTube stardom. 🎥 The art of storytelling: How Josh turns dense topics into clear, engaging narratives. 🧠 Simplifying the complex: How to focus on the main idea while avoiding information overload. 🔧 Practical advice: Using problem-first thinking to guide your learning and career growth. Whether you're a data professional, an aspiring data scientist, or someone curious about the power of storytelling in tech, this episode is packed with insights and inspiration. Oh, and did we mention the Triple BAM (and why there's no quadruple BAM)? Don’t miss this one-of-a-kind conversation with one of the most beloved educators in the AI and data science community. 🔗 Links Mentioned in the Episode StatQuest YouTube: https://www.youtube.com/c/joshstarmer StatQuest: https://statquest.org/ Follow Josh: https://www.linkedin.com/in/areganti/ Connect with Hai, Sravya, and Shane (let us know which platform sent you!): Hai: https://linkedin.openinapp.co/4qi1r Sravya: https://linkedin.openinapp.co/9be8c Shane: https://linkedin.openinapp.co/b02fe If you need personalized advice for your data science career roadmap, send us a message on LinkedIn or post a comment below!

Ever wondered how to transition into the AI engineering space or what skills truly matter in this fast-evolving field? In this episode of Data Neighbor Podcast, we sit down with Aishwarya Reganti, a Generative AI Tech Lead at Amazon's AWS, to dive into the essentials of AI engineering and separate the signal from the noise surrounding AI today. Aishwarya shares her journey from researcher to enterprise innovator and reveals: - AI Engineering practical tips, roadmaps, and myths busted. - How to get started with AI, whether you're a mid-career switcher or a recent graduate. - A clear roadmap of skills to master, including concepts like RAG, agents, and prompt engineering. - Practical advice for building AI projects that solve real problems and avoid unnecessary complexity. - How to stay focused amidst the overwhelming amount of AI content and buzzwords. Whether you're looking to break into the field, better understand the hype around AI, or learn how to apply AI concepts simply and effectively, this episode is packed with insights for you. - Links Mentioned in the Episode Aishwarya's Maven Course: https://lnkd.in/eiBx3Eaz DeepLearning.AI Free Courses: https://www.deeplearning.ai/ Tools Highlighted: NotebookLM, Claude, PotSmart, and more Follow Aishwarya: https://www.linkedin.com/in/areganti/ Connect with Hai, Sravya, and Shane (let us know the podcast platform sent you!): Hai: https://linkedin.openinapp.co/4qi1r Sravya: https://linkedin.openinapp.co/9be8c Shane: https://linkedin.openinapp.co/b02fe If you need personalized advice for your data science career roadmap, send us a message on LinkedIn or post a comment below! #ai #aiengineering #generativeai #artificialintelligence #techcareers #datascience #machinelearning #rag #promptengineering #techroadmap #careeradvice #innovation #aiprojects #techpodcast #dataneighbor #datapodcast #airoadmap #careeradvice #career #aiagents #finetuning

Curious about building a long-term career in data science? This episode of the Data Neighbor Podcast dives deep into actionable strategies and leadership insights for aspiring and experienced data professionals. Join us as we chat with Manisha Arora, a data science leader at Google and founder of PrepVector, as she shares her inspiring journey and practical advice. We cover: -The data science roadmap and how to plan for a successful career in data science and analytics. -Differentiating between managers and leaders in data roles. -Strategies for career growth in data science, including networking, coaching, and mentoring. -The importance of ownership mentality and transitioning from non-tech to tech roles. -Underrated skills in data science, from storytelling to prioritization. Whether you're exploring the data science roadmap in 2025, aiming for leadership in data science, or transitioning from a non-tech background, this episode is packed with insights to help you thrive in this evolving field. Connect with Manisha: https://www.linkedin.com/in/manisha-arora1/ Connect with Hai, Sravya, and Shane (let us know Apple or Spotify sent you!): Hai: https://linkedin.openinapp.co/4qi1r Sravya: https://linkedin.openinapp.co/9be8c Shane: https://linkedin.openinapp.co/b02fe If you need personalized advice for your data science career roadmap, send us a message on LinkedIn or post a comment below! #datascience #datascienceroadmap2025 #dataanalystroadmap2025 #datasciencecareer #datascienceleader #techpodcast #PrepVector #googledatasciience #growthmindset

Curious about AI but overwhelmed by the jargon? This episode of the Data Neighbor Podcast breaks down the evolution of artificial intelligence in a way anyone can understand! Join us as we chat with Karan Singh, a senior software engineer at Salesforce, to explore AI's journey from rule-based systems to generative AI like ChatGPT and large language models (LLMs). We also talked about whether AI can (or cannot) reason and why they're perceived to be intelligent. We cover: - The history of AI and how it evolved into tools like ChatGPT. - Can LLMs reason? - Why LLMs and AI Agents are revolutionary and how they’re used today. - The future of AI, including AGI (Artificial General Intelligence) and how it could reshape jobs. - Practical advice for breaking into the AI field - whether you’re technical or not! Whether you're a beginner in tech or just curious about where AI is headed, this episode is your crash course to understanding it all—simplified, engaging, and insightful. Connect with Karan: https://linkedin.openinapp.co/whh2h Connect with Hai, Sravya, and Shane (let us know YouTube sent you!): Hai: https://linkedin.openinapp.co/4qi1r Sravya: https://linkedin.openinapp.co/9be8c Shane: https://linkedin.openinapp.co/b02fe If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below! #ArtificialIntelligence #AI #ChatGPT #GenerativeAI #MachineLearning #DataScience #TechPodcast #futureofwork

Curious about the data science salary landscape? In this episode of Data Neighbor Podcast, we dive deep into data scientist salary insights, uncovering the salary of data scientists across industries. From data science compensation structures to a detailed tech salary breakdown, we explore everything you need to know about data scientist pay and data science jobs. Using reliable sources like levels.fyi salary, we provide a data scientist salary comparison and even tackle the question: data science vs doctor salary - who earns more? Whether you're aiming for high paying data science jobs, seeking data scientist compensation 2024 trends, or just curious about data science salary in tech, this episode has you covered. Discover tech salary secrets, tips for data science salary negotiation, and insights into data science industry salary trends. Don't miss this guide to data science salaries explained! Subscribe for more episodes where we explore data science, machine learning, data engineering, business intelligence, and career growth! Connect with Hai, Sravya, and Shane (let us know which streaming platform sent you!): Hai: https://linkedin.openinapp.co/4qi1r Sravya: https://linkedin.openinapp.co/9be8c Shane: https://linkedin.openinapp.co/b02fe If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below! Data science salary resources: Levels.fyi: https://www.levels.fyi/ Glassdoor: https://www.glassdoor.com/Salaries/index.htm LinkedIn: https://www.linkedin.com/ Candor: https://candor.co/guides/salary-negotiation

In this episode of the Data Neighbor Podcast, we chat with VK Xu, a data engineer at Meta, about her remarkable journey from a background in journalism to becoming a data engineer at one of the world’s leading tech companies. VK shares how she transitioned from being a non-technical data journalist to mastering coding, analytics, and engineering in the tech world. We dive into her experiences working at startups like Hoodline and Nextdoor, and how she embraced challenges, sought mentorship, and eventually made the leap to Meta. VK’s story is a testament to the power of adaptability, curiosity, and finding what truly excites you in your career. We explore key topics like: - VK’s pivot from journalism to data engineering - The power of mentorship and finding your strengths - Building a career at Nextdoor and transitioning to Meta - Navigating challenges in data engineering and AI’s impact Whether you're considering a career pivot, aspiring to break into data engineering, or curious about what it’s like to work at Meta, this episode is packed with practical advice, personal stories, and inspiration to help guide your own journey. Subscribe for more episodes where we explore data science, machine learning, data engineering, business intelligence, and career growth! Connect with VK: https://www.linkedin.com/in/huiqixu/ Connect with Hai, Sravya, and Shane: Hai: https://www.linkedin.com/in/hai-guan-6b58a7a Sravya: https://www.linkedin.com/in/sravyamadipalli Shane: https://www.linkedin.com/in/shaneausleybutler If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below!

In this episode of the Data Neighbor Podcast, we sit down with Nirmal Budhathoki, a senior data scientist at Microsoft, to uncover his fascinating journey from a small town in Nepal, to joining the military in the US, to leading impactful machine learning and data science projects in Big Tech. Nirmal shares how his unique career path, including his time in the military, shaped his approach to tackling complex data science problems, such as anomaly detection and machine learning applications in cybersecurity. We explore key topics like: - Nirmal’s unconventional career path - Lessons from his time in the military - Approaching complex data problems - Insights on transitioning into big tech Whether you're curious about data science jobs, aspiring to work on machine learning projects, or seeking to improve your data science skills, this conversation offers practical insights and inspiration. Don’t miss Nirmal’s tips on navigating career challenges and staying focused on learning. Subscribe for more episodes where we dive deep into the world of data science, machine learning, data engineering, business intelligence, data engineering, and career growth! Connect with Nirmal: https://www.linkedin.com/in/nirmal-budhathoki/ Connect with Hai, Sravya, and Shane Hai: https://www.linkedin.com/in/hai-guan-6b58a7a Sravya: https://www.linkedin.com/in/sravyamadipalli Shane: https://www.linkedin.com/in/shaneausleybutler If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below Spotify https://open.spotify.com/show/3BZnavSZaJ1VibgGa7uRH4 Apple https://podcasts.apple.com/us/podcast/data-neighbor-podcast/id1780934430

In this episode of the Data Neighbor Podcast, we sit down with Dawn Choo (aka Ask Data Dawn), a data scientist turned entrepreneur, to explore her fascinating journey from finance to big tech to building her own business. Dawn shares insights into navigating visa challenges, taking calculated risks (like a 40% pay cut!), and transitioning from a Business Analyst role to a Data Scientist at top companies like Amazon and Meta. Dawn also talked about how an unexpected turn of events led her to be on the NFL cheerleading squad in the Super Bowl. We also dive into: - The importance of self-promotion in corporate America - Dawn's bold career moves, including a 40% pay cut to break into tech - Overcoming rejections, imposter syndrome, and early failures - How creativity fuels technical careers - Why mentorship is a must-have - The value of relationships and communication in career progression Whether you're breaking into data science or looking to pivot your career, Dawn’s story will leave you inspired to embrace risk, stay consistent, and discover your unique superpower. Connect with Dawn: https://www.linkedin.com/in/data-dawn/ Connect with Hai, Sravya, and Shane Hai: https://www.linkedin.com/in/hai-guan-6b58a7a Sravya: https://www.linkedin.com/in/sravyamadipalli Shane: https://www.linkedin.com/in/shaneausleybutler If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below Spotify https://open.spotify.com/show/3BZnavSZaJ1VibgGa7uRH4 Apple https://podcasts.apple.com/us/podcast/data-neighbor-podcast/id1780934430

In this episode of the Data Neighbor Podcast, we sit down with Kartik Singhal (Senior Machine Learning Engineer at Meta) to explore his inspiring journey from a small town in India to working at some of the world’s top tech companies (Amazon, Google, and Meta). Kartik shares his struggles with insecurities, tips for building confidence, and the intentional decisions that shaped his career. You’ll hear how Kartik transitioned from software engineering to machine learning and the tough choices he made—like turning down a dream job at Google, turning down promotion opportunity and higher salary—and the framework he used to navigate career pivots. From building domain knowledge and leveraging his network to developing transferable skills, Kartik’s story is full of actionable insights for anyone looking to break into or advance in the tech field. This episode also dives into: - Why networking and persistence are critical to success. - How to make intentional career decisions that align with your long-term goals. Whether you’re starting out in software engineering, machine learning, or considering a career pivot, this conversation will leave you feeling inspired and equipped to tackle your next steps. Connect with Kartik: https://www.linkedin.com/in/kartiks93/ Connect with Hai, Sravya, and Shane Hai: https://www.linkedin.com/in/hai-guan-6b58a7a Sravya: https://www.linkedin.com/in/sravyamadipalli Shane: https://www.linkedin.com/in/shaneausleybutler If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below.

Sr. Director of Data Science Gene Tabach gets real about breaking into big tech, data science wins, and the secret sauce to career success. Connect with Gene: linkedin.com/in/genetabach Connect with Hai, Sravya, and Shane Hai: linkedin.com/in/hai-guan-6b58a7a Sravya: linkedin.com/in/sravyamadipalli Shane: linkedin.com/in/shaneausleybutler If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below

Discover how data scientists, analysts, analytics engineers, and ML engineers can prove their value by linking their work to one of three things - key metrics, roadmap directions, and process changes. Connect with Hai, Sravya, and Shane Hai: linkedin.com/in/hai-guan-6b58a7a Sravya: linkedin.com/in/sravyamadipalli Shane: linkedin.com/in/shaneausleybutler If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below

Join hosts Hai, Sravya, and Shane as they kick off the Data Neighbor Podcast, breaking down data science myths, sharing career journeys, and revealing how anyone can thrive in this field! Connect with Hai, Sravya, and Shane Hai: linkedin.com/in/hai-guan-6b58a7a Sravya: linkedin.com/in/sravyamadipalli Shane: linkedin.com/in/shaneausleybutler If you need personalized advice in your data career, send us a message on LinkedIn or post a comment below