PODCAST · technology
Value Driven Data Science
by Dr Genevieve Hayes
Your stakeholders don’t want a better model. They want a better decision.Value Driven Data Science is a masterclass for data professionals ready to become strategic experts, valued for their judgement, not just the technical outputs they produce.Each week, Dr Genevieve Hayes speaks with leading thinkers and data practitioners who have already made that shift. You’ll leave every episode with an idea or framework you can start using right away.If you’re tired of being treated as just another data scientist, Value Driven Data Science will transform you into the trusted advisor your stakeholders ask for by name.
Episodes
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Episode 128: [Value Boost] The Three Types of Memory Every AI Agent Needs
One of the most frustrating things about working with AI is when it seems to suddenly forget everything it knew about you and your work. This is annoying when using AI chatbots. When building AI systems, it can be catastrophic. In this Value Boost episode, Dr Imran Ahmad joins Dr Genevieve Hayes…
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Episode 127: From Algorithms to Agents and What It Means for Data Science
The agentic AI era hasn't made algorithms irrelevant. It has just changed where data scientists need to focus their attention. And the data scientists who understand that distinction, and can explain it to stakeholders who don't, are the ones who will define what the profession looks like next.…
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Episode 126: Why AI Excels at Some Things and Sucks at Others
Large language models excel at some tasks while struggling badly with other. At a time where it seems everyone is currently trying to make sense of AI, understanding and being able to explain exactly why this is the case, has become one of the most useful things data scientists can offer their...
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Episode 125: The AI Chicken Nugget Problem
Building something valuable with AI doesn't automatically make you valuable. If the value sits in your creation and your employer owns your creation, you're the one who's dispensable, not the thing you built. This is the AI chicken nugget problem and many data scientists who are impacted by it...
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Episode 124: [Value Boost] 8 Disciplines Every Decision System Needs
Machine learning is what most data scientists know best. But when it comes to building systems that enable better decision-making, machine learning is just one piece of the puzzle. And leaning on it too much can leave your entire decision system exposed. In this Value Boost episode, Adam DeJans…
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Episode 123: How to Build a Decision Factory Inside Your Organisation
Data scientists are trained to build better models. But stakeholders don't wake up hoping for a better model. They wake up hoping to make better decisions. And the gap between those two things is where most data science value quietly disappears. In this episode, Adam DeJans Jr joins Dr Genevieve...
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Episode 122: [Value Boost] 4 Questions Every Data Scientist Should Ask About AI Strategy
Choosing which AI tools to buy is the easy part of an AI strategy. The hard part - and the part most organisations are ignoring - is everything that sits beneath the tools, from platform and inference decisions to hardware and sovereignty. In this Value Boost episode, Victor Coimbra joins Dr...
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Episode 121: What Hybrid Agentic AI Organisations Mean for Data Science
The debate about whether AI will replace human workers has already been settled - not by academics or futurists, but by the organisations that fired their humans, discovered AI couldn't do what they needed, and quietly hired them back. The future isn't AI replacing humans. It's humans and AI...
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Episode 120: [Value Boost] The AI Silent Correctness Problem
AI hallucinations get all the attention. But hallucinations are relatively easy to catch because the output is obviously wrong. The failure mode that should worry data scientists more is when the agent uses facts that are true to draw conclusions that are false, producing outputs that look...
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Episode 119: Rewiring Your Data Science Thinking for the Agentic AI Era
The shift to agentic AI doesn't make data science skills obsolete. But it does require data scientists to rewire how they think about familiar concepts, such as uncertainty, model evaluation and accountability, in their work. In this episode, Jia Huang joins Dr Genevieve Hayes to explore what…
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Episode 118: [Value Boost] Compounding Your Data Science Authority Beyond Blog Posts
A well-written blog post gets you noticed. But for data scientists who want to build authority that compounds over time, it's just the beginning. Every piece of writing is a potential stepping stone to something bigger - a conference talk, a book deal, or an opportunity you couldn't have...
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Episode 117: Writing Your Way to Authority as a Data Scientist
For data scientists who want to build authority beyond their organisation, writing is one of the most powerful tools available. But in a world flooded with AI-generated content, simply publishing is no longer enough. The data scientists who stand out are the ones writing things no AI could have...
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Episode 116: [Value Boost] What Data Scientists Need to Know Before the AI Free Ride Ends
Right now, AI is cheap. But the companies providing it are losing billions of dollars a year and are expected to go public within the next 12 months. When they do, the price of intelligence is going up - and any organisation that has built its AI future entirely on rented intelligence is going to...
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Episode 115: Evolving Your Data Career for the AI Era
The AI era presents a choice for data professionals: wait to see what happens to your role, or get ahead of it and decide what it becomes. Nicholas Kelly made that choice two years ago, when a client told him ChatGPT could do 50% of what he did. What followed is a masterclass in proactive career...
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Episode 114: [Value Boost] The Four Conversations Every Data Scientist Needs to Master
For data scientists, getting a project approved is a sale. It might not feel like one and money might not change hands, but the dynamics are exactly the same. And like any sale, it goes a lot better if you go in with a plan. In this Value Boost episode, Blair Enns joins Dr Genevieve Hayes to...
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Episode 113: The Experts' AI Manifesto
Building genuine expertise takes years. And in the age of AI, losing it can happen gradually and almost invisibly, through small delegations that each seem reasonable in isolation but add up to something significant over time. In this episode, Blair Enns joins Dr Genevieve Hayes to explore how…
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Episode 112: [Value Boost] Lies, Damned Lies and Stakeholders
AI misinformation is a new problem. Misleading data is not. Long before anyone had heard of a hallucination, organisations were making bad decisions based on cherry-picked statistics, misunderstood averages, and numbers that confirmed what decision-makers already wanted to believe. In this Value...
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Episode 111: Building Your Defences Against AI Misinformation
AI doesn't lie - at least, not intentionally. It just sounds completely confident while filling in the gaps with whatever seems most plausible. And in a world where AI outputs are increasingly being used to inform high-stakes decisions, the ability to spot what's wrong, before it reaches a...
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Episode 110: [Value Boost] Why You Need Less Data Than You Think
In high-stakes decision-making, waiting for more data is often not an option. Yet many data scientists assume that without a large dataset, meaningful analysis is impossible. The good news is that rigorous, quantitative analysis is possible with far less data than most data scientists realise…
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Episode 109: How to Measure Anything and Make Better Decisions
Data scientists are trained to work with large datasets. But the decisions that truly make or break an organisation are rarely the ones with large datasets behind them. They are the high-stakes, one-off decisions made under significant uncertainty - and most data scientists have no framework for...
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Episode 108: [Value Boost] How to Use AI Without Losing Your Edge
AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes them valuable in the first place. In this Value Boost episode, Tim Dietrich joins Dr Genevieve Hayes to explore how to stay on the...
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Episode 107: Building a Virtual Empire of AI Specialists
The question haunting every data scientist right now isn't whether AI will change their work, it's whether there will still be a place for them when it does. The answer, according to Tim Dietrich, isn't to compete with AI but to do something far more interesting with it - in his case, building a...
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Episode 106: [Value Boost] When AI Isn't the Answer
These days, every organisation wants to describe themselves as "AI-first". But in the rush to find opportunities to use AI, it can be easy to forget that AI isn't always the right answer. In this Value Boost episode, Santosh Kaveti joins Dr Genevieve Hayes to explore the situations where AI isn't...
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Episode 105: From AI Idea to Production Reality
Organisations today have no shortage of AI ideas. What they lack is the ability to turn those ideas into production-ready systems that deliver real business value. For data scientists trying to get AI projects off the ground, understanding why that gap exists is as important as the technical work...
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Episode 104: [Value Boost] The Four Zones of AI Productivity for Data Scientists
AI can get you to 60% of a finished output in minutes. But getting from 60% to 100% - the part where real insight lives - is where human expertise becomes the deciding factor. And the more expertise you bring, the further AI can take you. In this Value Boost episode, Brent Dykes joins Dr…
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Episode 103: The Art of the Actionable Insight
Most data scientists have been in this situation: you spend hours analysing a dataset, return to your stakeholder with your findings, and are met with a polite "that's interesting" - before your work disappears into a drawer, never to be seen again. The problem usually isn't the analysis. It's…
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Episode 102: [Value Boost] How Giving Away Your Work for Free Can Build Your Authority as a Data Scientist
Building authority as a data professional doesn't require a large budget, a publisher, or even a large audience. But it does require a deliberate decision to share your thinking with the world and the patience to let that compound over time. In this Value Boost episode, Prof. Rob Hyndman joins…
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Episode 101: Why Traditional Statistics Still Matters in the Age of AI
Data scientists today are under pressure to adopt the latest tools - machine learning, LLMs, generative AI. But in the rush to embrace what's new, many are leaving some of the most powerful analytical tools sitting on the shelf. Tools that handle something modern AI largely can't: uncertainty. In...
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Episode 100: What Data Science Value Really Means
Over 100 episodes of conversations with world-class practitioners, a few ideas keep surfacing. Technical skill is necessary but never sufficient. The most valuable data professionals aren't the ones who build the best models - they're the ones who know which problems are worth solving. And the…
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Episode 99: [Value Boost] Preventing ML Bias Before it Becomes a Problem
Biased machine learning models don't just produce poor predictions. They can damage reputations, derail projects, and in high-stakes fields like healthcare, potentially cause real harm. Yet many data scientists don't check for bias until it's too late, missing the opportunity to address it at its...
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Episode 98: Building Trust in AI Through Model Interpretability
When your machine learning model makes a decision that affects someone's medical treatment, financial security, or legal rights, "the algorithm said so" isn't good enough. Stakeholders need to understand why models make the decisions they do, and in high-stakes environments, model…
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Episode 97: [Value Boost] Mathematical Modelling as a Gateway to ML Success
Data scientists often jump straight to machine learning when tackling a new problem. But there's a foundational step that can dramatically increase your chances of project success and create more reliable business value. Mathematical modelling from first principles provides a low-cost scaffolding...
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Episode 96: Making Better Decisions with ML and Optimisation
Data scientists use optimisation every day when training machine learning models, without even thinking about it. But there's another type of optimisation - that many data scientists are unaware of - that can be used to dramatically boost the business value of your ML outputs. This second layer...
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Episode 95: [Value Boost] Building Models That Work While Millions Are Watching
Building a model for an academic paper is one thing. Building a model that has to work perfectly during the Cricket World Cup with millions watching is something else entirely. There's no room for the kind of errors that might be acceptable in research settings or even standard business...
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Episode 94: Creating Global Impact with Data Science
For most data scientists, the idea of impacting the world through your work seems impossible. You may be developing technically brilliant solutions within your organisation, but seeing them become industry standards or influence global decisions feels completely out of reach. In this episode…
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Episode 93: [Value Boost] What Industry Data Scientists Can Learn from Academic Training
While the transition from academia to industry can be brutal for data scientists, academics don't show up in industry empty-handed. They bring powerful transferable skills that many industry-trained data scientists never develop. In this Value Boost episode, Dr. Sayli Javadekar joins Dr.…
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Episode 92: Making the Academia to Industry Leap in Data Science
Making the leap from academia to industry isn't just another career change - it involves a complete shift in the way you work. Data scientists transitioning from academia face a brutal learning curve that can leave them feeling unprepared despite years of advanced training. In this episode, Dr....
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Episode 91: [Value Boost] How Your Hobbies Can Supercharge Your Data Science Career
Activities outside of data science can strengthen the very skills data scientists need for their careers in surprising ways. From improving stakeholder communication to learning how to work with resistance rather than against it, hobbies and interests often teach lessons that directly translate…
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Episode 90: Using LLMs to Become a More Effective Data Scientist
When most data scientists think about using LLMs and generative AI, the first thing that springs to mind is writing code faster. While that's certainly useful, if it's the only application you're exploring, you're missing some of the most powerful opportunities to enhance your effectiveness as a...
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Episode 89: [Value Boost] LinkedIn Strategies for Boosting Your Data Science Career
LinkedIn has become a powerful career tool for data scientists willing to invest the time. Regular posting can lead to unexpected work opportunities, reconnections with former colleagues, and valuable networking with professionals worldwide. But making the leap from occasional posting to…
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Episode 88: Building a Data Science Career After Unexpected Job Loss
There was once a time, when data science was still in its infancy, when demonstrating any attempt to learn Python or machine learning was enough to secure a job interview. The demand for data scientists massively outweighed supply. Ten years later, however, the job market has dramatically shifted…
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Episode 87: [Value Boost] How Your Weirdness Could Be Your Data Science Superpower
When most data scientists think about their competitive edge, they focus solely on what goes on their resume - education, work experience, and technical skills. But what if the things that truly make you irreplaceable go far deeper than your LinkedIn profile? Your family background, cultural...
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Episode 86: Why Every Data Scientist Is Already Running a Business
Every data scientist is running their own business - it's just that most of those businesses are solo operations with one client: their employer. Unfortunately, most data scientists don't realise this and too many fall into the trap of believing their employer will magically take care of their...
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Episode 85: [Value Boost] The Office Politics Survival Guide for Data Science Experiments
Here's something that data science courses don't prepare you for: even your most brilliant analysis can fail if you can't navigate the human side of your organisation. And office politics becomes especially tricky when you're running experiments. You're essentially asking people to place bets on...
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Episode 84: The 7-Step Checklist for Creating Business Impact Through Product Analytics
When working with data, it can be easy to fall into the trap of believing that your dataset represents nothing more than numbers on a page. However, behind every data point is a human story - people clicking through websites, abandoning shopping carts, or binge-watching Netflix shows. And in our...
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Episode 83: [Value Boost] How to Gamify Data Science Requirements Gathering for Better Results
Stakeholder requirement gathering is often one of the most dreaded parts of data science projects - dry, tedious sessions where conflicting voices talk past each other and senior executives dominate the conversation. Yet without proper requirements, data science projects are doomed to fail due to...
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Episode 82: Why You Should Start Your Data Projects with Pictures Not Data
Most data scientists follow the same predictable process: gather requirements, collect data, build models, and only at the very end create visualisations to communicate results. This traditional approach seems logical, but what if it's actually working against us? In this episode, David Cohen…
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Episode 81: [Value Boost] How to Frame Data Problems Like a Decision Scientist
Data science training programs often jump straight into technical methods without teaching one of the most critical skills for project success - problem framing. Without proper framing, data science projects are doomed to fail, right from the start, as data scientists find themselves solving the...
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Episode 80: Why Decision Scientists Succeed Where Data Scientists Fail
Most data scientists have never heard of decision science, yet this discipline - which dates back to WWII - may hold the key to solving one of data science's biggest problems: the 87% project failure rate. While data scientists excel at building models that predict outcomes, decision scientists...
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Episode 79: [Value Boost] The Win Win Data Product Validation Strategy
One of the biggest risks for independent data professionals is spending months or years developing a product or service that nobody wants to buy. The graveyard of failed data science projects is filled with technically brilliant solutions that solved problems no one actually had, leaving their...
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ABOUT THIS SHOW
Your stakeholders don’t want a better model. They want a better decision.Value Driven Data Science is a masterclass for data professionals ready to become strategic experts, valued for their judgement, not just the technical outputs they produce.Each week, Dr Genevieve Hayes speaks with leading thinkers and data practitioners who have already made that shift. You’ll leave every episode with an idea or framework you can start using right away.If you’re tired of being treated as just another data scientist, Value Driven Data Science will transform you into the trusted advisor your stakeholders ask for by name.
HOSTED BY
Dr Genevieve Hayes
CATEGORIES
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Frequently Asked Questions
How many episodes does Value Driven Data Science have?
Value Driven Data Science currently has 127 episodes available on PodParley. New episodes are automatically indexed when they're published to the podcast feed.
What is Value Driven Data Science about?
Your stakeholders don’t want a better model. They want a better decision.Value Driven Data Science is a masterclass for data professionals ready to become strategic experts, valued for their judgement, not just the technical outputs they produce.Each week, Dr Genevieve Hayes speaks with leading...
How often does Value Driven Data Science release new episodes?
Value Driven Data Science publishes regularly with 127 episodes available. Check the episode list for the typical release schedule.
Where can I listen to Value Driven Data Science?
You can listen to Value Driven Data Science on PodParley by clicking any episode. We provide an embedded audio player for direct listening, and you can also subscribe via your preferred podcast app using the RSS feed.
Who hosts Value Driven Data Science?
Value Driven Data Science is created and hosted by Dr Genevieve Hayes.
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