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All Episodes

DataTalks.Club — 213 episodes

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Title
1

Competitions: Beyond the Kaggle Leaderboard - Tatiana Habruseva

2

PyConDE 2026 Conference Interviews

3

Starting a Data Conference: The Data Makers Fest Story - Leonid Kholkine

4

Understanding the AI Engineer Role - Nasser Qadri

5

Data Engineer Career in 2026: Roles, Specializations, and What Companies Look for - Slawomir Tulski

6

Inside the AI Engineer Role: Tools, Skills, and Career Path - Ruslan Shchuchkin

7

How to Become an AI Engineer After a Career Break - Revathy Ramalingam

8

The Future of AI Agents - Aditya Gautam

9

Foundations of Analytics Engineer Role: Skills, Scope, and Modern Practices - Juan Manuel Perafan

10

AI Engineering: Skill Stack, Agents, LLMOps, and How to Ship AI Products - Paul Iusztin

11

Applying ML: An Ongoing Personal Journey

12

Building Pet Health Tech: ML, Sensors, and Dog Behavior Data

13

From Full-Time Mom to Head of Data and Cloud - Xia He-Bleinagel

14

From Black-Box Systems to Augmented Decision-Making - Anusha Akkina

15

Qdrant 2025 Conference Interviews

16

How to Build and Evaluate AI systems in the Age of LLMs - Hugo Bowne-Anderson

17

From Biotechnology to Bioinformatics Software - Sebastian Ayala Ruano

18

Lessons from Applied AI: Tesla, Waymo, and Beyond - Aishwarya Jadhav

19

Building reliable AI products in the era of Gen AI and Agents - Ranjitha Kulkarni

20

From Theme Parks to Tesla: Building Data Products That Work

21

From Semiconductors to Machine Learning: A Career in Data and Teaching

22

Lessons from Two Decades of AI - Micheal Lanham

23

Berlin PyData 2025 Conference Interviews

24

From Astronomy to Applied ML - Daniel Egbo

25

Berlin Buzzwords 2025 Conference Interviews

26

From Medicine to Machine Learning: How Public Learning Turned into a Career - Pastor Soto

27

How to Rebuild Data Trust? Mindful Data Strategy and Maintenance vs Innovation - Lior Barak

28

From Simulations to Freelance Data Engineering: Orell's Journey Out of Academia and Into Consulting - Orell Garten

29

Can You Quit Your Job and Still Succeed as a Data Freelancer?

30

From Hackathons to Developer Advocacy - Will Russel

31

Build a Strong Career in Data - Lavanya Gupta

32

From Supply Chain Management to Digital Warehousing and FinOps - Eddy Zulkifly

33

Data Intensive AI - Bartosz Mikulski

34

MLOps in Corporations and Startups - Nemanja Radojkovic

35

Trends in Data Engineering – Adrian Brudaru

36

Competitive Machine Leaning And Teaching – Alexander Guschin

37

Redefining AI Infrastructure: Open-Source, Chips, and the Future Beyond Kubernetes – Andrey Cheptsov

38

Linguistics and Fairness - Tamara Atanasoska

39

Career choices, transitions and promotions in and out of tech - Agita Jaunzeme

40

Career advice, learning, and featuring women in ML and AI - Isabella Bicalho

41

AI in Industry: Trust, Return on Investment and Future - Maria Sukhareva

42

Large Hadron Collider and Mentorship – Anastasia Karavdina

43

MLOps as a Team - Raphaël Hoogvliets

44

Using Data to Create Liveable Cities - Rachel Lim

45

DataTalks.Club 4th Anniversary AMA Podcast – Alexey Grigorev and Johanna Bayer

46

Human-Centered AI for Disordered Speech Recognition - Katarzyna Foremniak

47

DataOps, Observability, and The Cure for Data Team Blues - Christopher Bergh

48

Working as a Core Developer in the Scikit-Learn Universe - Guillaume Lemaître

49

Building a Domestic Risk Assessment Tool - Sabina Firtala

50

Berlin Buzzwords 2024

51

Community Building and Teaching in AI & Tech - Erum Afzal

52

Working in Open Source - Probabl.ai and sklearn - Vincent Warmerdam

53

AI for Ecology, Biodiversity, and Conservation - Tanya Berger-Wolf

54

Knowledge Graphs and LLMs Across Academia and Industry - Anahita Pakiman

55

Inclusive Data Leadership Coaching - Tereza Iofciu

56

Building Production Search Systems - Daniel Svonava

57

Building Machine Learning Products - Reem Mahmoud

58

Make an Impact Through Volunteering Open Source Work - Sara EL-ATEIF

59

Accelerating The Job Hunt for The Perfect Job in Tech - Sarah Mestiri

60

Machine Learning Engineering in Finance - Nemanja Radojkovic

61

Stock Market Analysis with Python and Machine Learning - Ivan Brigida

62

Bayesian Modeling and Probabilistic Programming - Rob Zinkov

63

Navigating Challenges and Innovations in Search Technologies - Atita Arora

64

The Entrepreneurship Journey: From Freelancing to Starting a Company - Adrian Brudaru

65

Become a Data Freelancer - Dimitri Visnadi

66

AI for Digital Health - Maria Bruckert

67

Cracking the Code: Machine Learning Made Understandable - Christoph Molnar

68

The Unwritten Rules for Success in Machine Learning - Jack Blandin

69

From a Research Scientist at Amazon to a Machine learning/AI Consultant - Verena Webber

70

From Marketing to Product Owner in Search - Lera Kaimashnіkova

71

Collaborative Data Science in Business - Ioannis Mesionis

72

Bridging Data Science and Healthcare - Eleni Stamatelou

73

DataTalks.Club Anniversary Interview - Alexey Grigorev, Johanna Bayer

74

Data Engineering for Fraud Prevention - Angela Ramirez

75

From Data Manager to Data Architect - Loïc Magnien

76

Pragmatic and Standardized MLOps - Maria Vechtomova

77

Democratizing Causality - Aleksander Molak

78

Mastering Data Engineering as a Remote Worker - José María Sánchez Salas

79

The Good, the Bad and the Ugly of GPT - Sandra Kublik

80

LLMs for Everyone - Meryem Arik

81

Investing in Open-Source Data Tools - Bela Wiertz

82

Why Machine Learning Design is Broken - Valerii Babushkin

83

Interpretable AI and ML - Polina Mosolova

84

From Scratch to Success: Building an MLOps Team and ML Platform - Simon Stiebellehner

85

From MLOps to DataOps - Santona Tuli

86

Data Developer Relations - Hugo Bowne-Anderson

87

Lessons Learned from Freelancing and Working in a Start-up - Antonis Stellas

88

Data Access Management - Bart Vandekerckhove

89

Data Strategy: Key Principles and Best Practices - Boyan Angelov

90

Practical Data Privacy - Katharine Jarmul

91

Building Scalable and Reliable Machine Learning Systems - Arseny Kravchenko

92

Building an Open-Source NLP Tool - Johannes Hötter

93

Navigating Industrial Data Challenges - Rosona Eldred

94

Mastering Self-Learning in Machine Learning - Aaisha Muhammad

95

The Secret Sauce of Data Science Management - Shir Meir Lador

96

SE4ML - Software Engineering for Machine Learning - Nadia Nahar

97

Starting a Consultancy in the Data Space - Aleksander Kruszelnicki

98

Biohacking for Data Scientists and ML Engineers - Ruslan Shchuchkin

99

Analytics for a Better World - Parvathy Krishnan

100

Accelerating the Adoption of AI through Diversity - Dânia Meira

101

Staff AI Engineer - Tatiana Gabruseva

102

The Journey of a Data Generalist: From Bioinformatics to Freelancing - Jekaterina Kokatjuhha

103

Navigating Career Changes in Machine Learning - Chris Szafranek

104

Preparing for a Data Science Interview - Luke Whipps

105

Indie Hacking - Pauline Clavelloux

106

Doing Software Engineering in Academia - Johanna Bayer

107

Data-Centric AI - Marysia Winkels

108

Business Skills for Data Professionals - Loris Marini

109

From Software Engineer to Data Science Manager - Sadat Anwar

110

Teaching and Mentoring in Data Analytics - Irina Brudaru

111

Technical Writing and Data Journalism - Angelica Lo Duca

112

From Digital Marketing to Analytics Engineering - Nikola Maksimovic

113

Product Owners in Data Science - Anna Hannemann

114

Building Data Science Practice - Andrey Shtylenko

115

Large-Scale Entity Resolution - Sonal Goyal

116

From Data Science to DataOps - Tomasz Hinc

117

Data Science Career Development - Katie Bauer

118

From Testing Phones to Managing NLP Projects - Alvaro Navas Peire

119

Responsible and Explainable AI - Supreet Kaur

120

Building Data Science Practice - Andrey Shtylenko

121

No episode this week

122

Leading Data Research - David Bader

123

Dataset Creation and Curation - Christiaan Swart

124

Data Mesh 101 - Zhamak Dehghani

125

Growing Data Engineering Team in a Scale-Up - Mehdi OUAZZA

126

Lessons Learned About Data & AI at Enterprises - Alexander Hendorf

127

MLOps Architect - Danny Leybzon

128

Decoding Data Science Job Descriptions - Tereza Iofciu

129

Data Science for Social Impact - Christine Cepelak

130

Hiring Data Science Talent - Olga Ivina

131

From Open-Source Maintainer to Founder - Will McGugan

132

Designing a Data Science Organization - Lisa Cohen

133

Developer Advocacy Engineer for Open-Source - Merve Noyan

134

Data Scientists at Work - Mısra Turp

135

Freelancing and Consulting with Data Engineering - Adrian Brudaru

136

Getting a Data Engineering Job (Summary and Q&A) - Jeff Katz

137

Using Data for Asteroid Mining - Daynan Crull

138

Machine Learning in Marketing - Juan Orduz

139

From Academia to Data Analytics and Engineering - Gloria Quiceno

140

Teaching Data Engineers - Jeff Katz

141

From Roasting Coffee to Backend Development - Jessica Greene

142

Recruiting Data Engineers - Nicolas Rassam

143

Storytime for DataOps - Christopher Bergh

144

Machine Learning and Personalization in Healthcare - Stefan Gudmundsson

145

Innovation and Design for Machine Learning - Liesbeth Dingemans

146

Hacking Your Data Career - Marijn Markus

147

Visualising Machine Learning - Meor Amer

148

From Math Teacher to Analytics Engineer - Juan Pablo

149

From Data Science to Data Engineering - Ellen König

150

Becoming a Data Engineering Manager - Rahul Jain

151

A/B Testing - Jakob Graff

152

Machine Learning System Design Interview - Valerii Babushkin

153

Career Coaching - Lindsay McQuade

154

Product Management Essentials for Data Professionals - Greg Coquillo

155

Recruiting Data Professionals - Alicja Notowska

156

DataTalks.Club Behind the Scenes - Eugene Yan, Alexey Grigorev

157

DTC's minis - From Data Engineering to MLOps - Sejal Vaidya

158

Becoming a Data Science Manager - Mariano Semelman

159

Leading NLP Teams - Ivan Bilan

160

Product Management for Machine Learning - Geo Jolly

161

Moving from Academia to Industry - CJ Jenkins

162

Advancing Big Data Analytics: Post-Doctoral Research - Eleni Tzirita Zacharatou

163

Becoming a Data Product Manager - Sara Menefee

164

Data Science Manager vs Data Science Expert - Barbara Sobkowiak

165

Ace Non-Technical Data Science Interviews - Nick Singh

166

Becoming a Solopreneur in Data - Noah Gift

167

Building Business Acumen for Data Professionals - Thom Ives

168

Conquering the Last Mile in Data - Caitlin Moorman

169

Similarities and Differences between ML and Analytics - Rishabh Bhargava

170

Building and Leading Data Teams - Tammy Liang

171

What Researchers and Engineers Can Learn from Each Other - Mihail Eric

172

Introducing Data Science in Startups - Marianna Diachuk

173

Defining Success: Metrics and KPIs - Adam Sroka

174

Making Sense of Data Engineering Acronyms and Buzzwords - Natalie Kwong

175

Mastering Algorithms and Data Structures - Marcello La Rocca

176

Chief Data Officer - Marco De Sa

177

Freelancing in Machine Learning - Mikio Braun

178

Launching a Startup: From Idea to First Hire - Carmine Paolino

179

Approach Learning as ML Project - Vladimir Finkelshtein [mini]

180

Humans in the Loop - Lina Weichbrodt

181

Running from Complexity - Ben Wilson

182

I Want to Build a Machine Learning Startup! - Elena Samuylova

183

Big Data Engineer vs Data Scientist - Roksolana Diachuk

184

Build Your Own Data Pipeline - Andreas Kretz

185

From Software Engineering to Machine Learning - Santiago Valdarrama

186

Analytics Engineer: New Role in a Data Team - Victoria Perez Mola

187

Data Governance - Jessi Ashdown, Uri Gilad

188

What Data Scientists Don’t Mention in Their LinkedIn Profiles - Yury Kashnitsky

189

Becoming a Data-led Professional - Arpit Choudhury

190

How to Market Yourself (without Being a Celebrity) - Shawn Swyx Wang

191

From Physics to Machine Learning - Tatiana Gabruseva

192

What I Learned After Interviewing 300 Data Scientists - Oleg Novikov

193

Effective Communication with Business for Data Professionals - Lior Barak

194

Data Observability - Barr Moses

195

Shifting Career from Analytics to Data Science - Andrada Olteanu

196

Transitioning from Project Management to Data Science - Ksenia Legostay

197

Building Online Tech Communities - Demetrios Brinkmann

198

DataOps 101 - Lars Albertsson

199

The Essentials of Public Speaking for Career in Data Science - Ben Taylor

200

New Roles and Key Skills to Monetize Machine Learning - Vin Vashishta

201

Personal Branding - Admond Lee Kin Lim

202

The ABC’s of Data Science - Danny Ma

203

Translating ML Predictions Into Better Real-World Results with Decision Optimization - Dan Becker

204

Feature Stores: Cutting through the Hype - Willem Pienaar

205

The Rise of MLOps - Theofilos Papapanagiotou

206

Getting Started with Open Source - Vincent Warmerdam

207

Developer Advocacy for Data Science - Elle O'Brien

208

The Importance of Writing in a Tech Career - Eugene Yan

209

Mentoring - Rahul Jain

210

Standing out as a Data Scientist - Luke Whipps

211

Building a Data Science Team - Dat Tran

212

Processes in a Data Science Project - Alexey Grigorev

213

Roles in a data team - Alexey Grigorev