Eric Ma is a Senior Principal Data Scientist at Moderna, where he leads the Data Science and Artificial Intelligence (Research) team. Previously, he conducted biomedical data science research at the Novartis Institutes for Biomedical Research and earned his doctorate in Biological Engineering from MIT. Eric is also an open-source software developer known for leading pyjanitor and nxviz and contributing to NetworkX and PyMC.

Dan and Eric discuss Eric's rapidly changing practice of agentic data science, centered on Marimo Pair and reproducible Python notebooks. Eric demonstrates an analysis of protein activity and stereoselectivity data, explains why canonical sources and inline environment metadata matter, and shows how agents make ambitious custom visualizations more approachable even when a live demo fails. The conversation then turns to design documents, testable specifications, repository conventions, and the attention-management practices Eric uses to keep agent work from becoming a context-switching trap.

Full episode notes

Transcript

Chapters

  • (00:00) - Why Eric's data science workflow changed so quickly
  • (05:49) - Marimo Pair puts agents inside the notebook
  • (07:19) - Reproducing a protein-engineering paper
  • (10:45) - Why enzyme stereoselectivity matters
  • (13:20) - Canonical data sources and portable environments
  • (18:08) - Exploring activity and selectivity with interactive plots
  • (27:47) - Finding promising regions for protein engineering
  • (30:24) - From heat maps to a 3D protein viewer
  • (35:54) - Learning from agent traces and preserving intent
  • (47:13) - Opinionated structure for agent-written code
  • (52:25) - Managing attention across agentic tasks

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Eric Ma, Senior Principal Data Scientist, Moderna

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