Summary
Ravi Sojitra is a Research Data Scientist and final-year PhD candidate at Stanford specializing in causal inference and machine learning, advised by Guido Imbens and Vasilis Syrgkanis. With nine years of experience spanning academia and industry, he has applied causal methods to platform problems like network effects and dynamic treatment regimes at Stanford, Meta, and Microsoft Research. Currently at Google working on AI metrics, he brings a rare blend of rigorous theoretical training and hands-on applied science across large-scale product settings. His background includes internships in applied search marketing at Amazon and teaching graduate courses on prediction, inference, and causality, reflecting both technical depth and pedagogy. Trained originally in biology and philosophy, Ravi combines interdisciplinary thinking with advanced statistics from NYU to tackle practical questions about cause and effect in complex systems.
9 years of coding experience
4 years of employment as a software developer
B.A. in Biology & Philosophy, B.A. in Biology & Philosophy at Rutgers University–Newark
M.S. in Applied Statistics for Social Science Research, M.S. in Applied Statistics for Social Science Research at New York University
Doctor of Philosophy - PhD, Management Science & Engineering, Doctor of Philosophy - PhD, Management Science & Engineering at Stanford University