Video by Open Data Science and AI Conference via YouTube
What does it mean to be a data scientist in the age of AI agents?
In this episode of the ODSC Ai X Podcast, Sheamus McGovern sits down with Eric Ma, Data Science Leader at Moderna, to explore how AI coding agents are changing the way data scientists work—and why human expertise matters more than ever.
Eric shares practical insights on agentic data science, coding agents, loop engineering, evaluation frameworks, fine-tuning, AI engineering, and why data scientists should focus on the science of measurement rather than trying to become full-stack software engineers.
00:00 Why Data Scientists Should Focus on Measurement
00:56 Introduction and ODSC AI West
02:10 Meet Eric Ma: From Bioengineering to Data Science
03:44 What Agentic Data Science Means in 2026
05:07 Delegating Analysis to Coding Agents
06:13 Why Data Scientists Still Need to Work by Hand
08:13 What Should Data Scientists Still Do Themselves?
10:14 Slowing Down and Verifying AI-Generated Analysis
12:51 How AI Coding Tools Free Up Cognitive Capacity
13:46 Using AI to Learn New Technical Domains
15:54 Does AI Widen the Gap Between Experts and Beginners?
18:10 The Most Important AI Skills for Data Scientists
20:12 Loop Engineering and Precise Agent Instructions
22:12 Thinking in Systems and Designing Feedback Loops
24:35 What Is an Agentic Harness?
25:01 Coding Agents, Marimo Notebooks, and Pair Programming
26:29 Building a Personal Knowledge Base with AI
29:31 When Should Teams Fine-Tune an LLM?
30:31 Cost, Privacy, and Security Considerations
32:19 Common Failure Modes in Agentic Data Science
34:03 Should Data Scientists Become Full-Stack Engineers?
35:17 Why Measurement Is the Core Data Science Skill
36:52 Data Scientists vs. AI Engineers
38:23 Essential Practices for Working with AI Agents
40:25 Will the Data Scientist Role Still Exist in 10 Years?
41:34 How Important Are Engineering Skills for Data Scientists?
42:09 Final Takeaways and Closing
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