Speaker Profile

Daniel Lee is VP of Sports Analytics and Principal Data Scientist at PyMC Labs. He is a computational Bayesian statistician and one of the original developers of Stan, the influential open-source probabilistic programming language for Bayesian inference. With more than two decades of experience in numerical computation and software, and over a decade specializing in Bayesian methods, Daniel has built applied models across professional sports and industry. He previously led player evaluation and projection work at Zelus Analytics / Teamworks, supporting elite teams with analytics for front-office decision making.

His work spans sabermetrics for a Major League Baseball team, forecasting demand for Warner Music Group, and Bayesian modeling in election forecasting, clinical research, and defense applications—often focused on decision-making under uncertainty. At the MIT Sloan Sports Analytics Conference, Daniel won the 2022 Hackathon for his work developing creative solutions to real-world sports data challenges. He holds a B.S. in Mathematics with Computer Science from MIT and a Master of Advanced Studies in Statistics from Cambridge University.