EPISODE · Jan 16, 2025 · 46 MIN
Predicting Responsibly: Claudia Perlich on AI, Bias, and the Art of Data Science
from Women in Data Science Worldwide · host Claudia Perlich, Professor Margot Gerritsen
Predictive Modeling (4:15) Human judgement and processes (14:06)Imperfection in models (21:40)BioClaudia Perlich is Managing Director and Head of Strategic Data Science for Investment Management at Two Sigma, where she has worked for seven years. In this role, Claudia is responsible for developing innovative alpha strategies at the intersection of alternative data, thematic hypotheses and machine learning in public markets. Claudia joined Two Sigma from Dstillery, an AI ad targeting company, where she worked as Chief Scientist. Claudia began her career in data science at the IBM Watson Research Center, concentrating on research in data analytics and machine learning for complex real-world domains and applications.Since 2011, Claudia has served as an adjunct professor teaching Data Mining in the M.B.A. program at New York University’s Stern School of Business. Claudia received a Ph.D. in Information Systems from Stern School of Business, New York University, holds an M.S. of Computer Science from Colorado University and a B.S. in Computer Science from Technical University Darmstadt, Germany. Connect with ClaudiaClaudia Perlich on LinkedinConnect with UsMargot Gerritsen on LinkedInFollow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher
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What this episode covers
Claudia Perlich, a leading expert in predictive modeling shares about her work and views on the challenges of using AI and machine learning systems. Perlich discusses the importance of understanding the uncertainty and biases inherent in these models, and the need for careful evaluation and testing before deploying them, especially in high-stakes domains. She emphasizes the collective responsibility of data scientists, domain experts, and decision-makers to ensure these systems are used responsibly and equitably. She also shares insights on the skills and mindset needed for a successful career in predictive modeling, highlighting the value of data curiosity, scientific thinking, and effective communication.
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Predicting Responsibly: Claudia Perlich on AI, Bias, and the Art of Data Science
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