Summary
Dorian Goldman is a Staff Applied Scientist with a decade of experience applying rigorous math and causal inference to real-world product and marketplace problems, currently modernizing risk systems at Coinbase while also leading AI-driven insulin delivery work as Chief Scientist at InsuLearn. He previously led technical strategy at Lyft, where his causal-effect libraries and novel long-term surrogate models produced company-wide metrics like "ride quality" and informed national optimization and competitive turnarounds. A former Herchel Smith Fellow and PhD-trained mathematician, he blends deep PDE/variational analysis with hands-on ML engineering—producing patented algorithms for shared pickup optimization and shipping production libraries used across large teams. He teaches applied data science at Columbia, favoring mathematically rigorous but practical courses, and has a track record of turning theoretical tools into measurable business impact. Notably, his career bridges pure mathematics and product ML, enabling solutions that account for network effects and long-term causal outcomes rather than short-term proxies.
10 years of coding experience
14 years of employment as a software developer
M.Sc Mathematics, M.Sc Mathematics at University of Toronto
Doctor of Philosophy (PhD) Mathematics, Doctor of Philosophy (PhD) Mathematics at NYU Courant Institute School of Mathematics, Computing, and Data Science
PhD Applied Mathematics, PhD Applied Mathematics at Pierre and Marie Curie University
Mathematics, Mathematics at École normale supérieure de Lyon
English, French