Chris Wiggins, Chief Data Scientist at The New York Times and a Columbia University professor, discusses the transition from predictive to prescriptive analytics. He emphasizes the importance of actionable decision systems, highlighting how hospitals could benefit from prescription-based treatments. Wiggins introduces the AI Hierarchy of Needs, outlines strategies for scaling data teams, and underlines the necessity of empathy in data science for effective collaboration. His insights help bridge the gap between advanced technology and practical organizational applications.
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question_answer ANECDOTE
Joining The Times
Chris Wiggins joined The New York Times after a sabbatical where he experimented with machine learning for subscription services.
This led to building a data science team at the Times, transitioning from writing code to writing emails.
insights INSIGHT
Prescriptive Analytics over Predictions
Predicting outcomes is insufficient; focus on prescribing actions to optimize them.
Shift from descriptive and predictive analytics towards prescriptive analytics, focusing on interventions.
insights INSIGHT
Software-Driven Decisions
Many companies operate within software, making decisions at scale.
Product interventions can be framed as code, allowing for stochastic optimization.
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In this seminal work, D'Arcy Wentworth Thompson critiques traditional biological approaches that rely on teleological interpretations and instead advocates for empirical and mechanical explanations for organic forms. The book explores how physical forces such as gravity, surface tension, and scale influence the shapes and structures of living organisms. It covers a wide range of topics, including the effects of scale on animal and plant shapes, the arrangement of leaves, and the logarithmic spirals in mollusk shells. Thompson's work revolutionized the field of biology by integrating physical science into the study of biological forms and growth.
How Data Happened
A History from the Age of Reason to the Age of Algorithms
Chris Wiggins
Making Sense of Life
Making Sense of Life
Evelyn Fox KELLER
The Book of Why
The New Science of Cause and Effect
Mel Foster
Dana Mackenzie Judea Pearl
Dana Mackenzie
Judea Pearl
In 'The Book of Why', Judea Pearl and Dana Mackenzie delve into the causal revolution, which has transformed the way we distinguish between correlation and causation. The book introduces causal diagrams, such as Directed Acyclic Graphs (DAGs), and explains how to predict the effects of interventions. It addresses fundamental questions about causality and its implications in fields like medicine, economics, and artificial intelligence. The authors also discuss the potential of causal inference in enabling computers to understand counterfactuals and engage in moral decision-making[2][4][5].
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 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.
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