In this episode of High Signal, Chris Wiggins—Chief Data Scientist at The New York Times, Professor at Columbia University, and co-author of How Data Happened—shares how organizations can move beyond prediction to actionable decision systems. Drawing on his work at The New York Times and in academia, Chris explains how to scale data teams, optimize systems, and align data science with organizational impact.
Key topics from the conversation include:
- From Prediction to Prescription: Why organizations need to focus on interventions that drive outcomes, illustrated with insights like, “Imagine a hospital prescribing treatments instead of just diagnosing conditions.”
- The AI Hierarchy of Needs: Foundational practices, such as data logging and engineering, that enable advanced machine learning and AI.
- Personalization and Optimization: How reinforcement learning and exploration-exploitation methods can help optimize KPIs and adapt to user context.
- Scaling Data Teams: Strategies for attracting and retaining talent by emphasizing autonomy, mastery, and purpose.
- Empathy as a Data Science Skill: The importance of collaborating with other teams and understanding their goals to drive adoption and success.
🎧 Tune in to learn how to build decision systems, integrate causality into workflows, and develop scalable data science teams for real-world impact.
You can find more on our website: https://high-signal.delphina.ai/
00:00 Introduction to Chris Wiggins' Journey
00:07 Building Data Functions at The New York Times
00:48 Early Challenges and Evolution
01:07 The Importance of Prescriptive Analytics
02:07 Optimization and Personalization
03:25 AI Hierarchy of Needs
04:01 Effective Data Science Teams
08:02 Chris Wiggins' Career Journey
14:01 Building a Data Function at The New York Times
16:44 Predictive to Prescriptive Analytics
22:03 History of Data Science
23:46 Statistics and Power
27:00 Building a Data Function: Practical Insights
39:42 Choosing the Right Tool for the Job in Machine Learning
40:35 Causal Inference and Reinforcement Learning
42:02 The Importance of Randomized Control Trials
43:59 Principal Component Analysis and Data Function Priorities
46:47 Empathy in Data Leadership
52:42 Generative AI in Education
58:05 Interdisciplinary Collaboration in Academia and Industry
01:10:34 Future of Data Science at The New York Times
01:15:11 Closing Thoughts and Advice for Data Science Leaders