I am an economist applying causal inference and machine learning to go-to-market and consumption-based revenue problems at AWS. I build measurement frameworks and predictive systems—difference-in-differences, synthetic control, regression discontinuity, survival analysis, gradient boosting, and transformer-based causal inference—that turn commercial data into business decisions across customer acquisition, retention, and growth. You can find more details on my LinkedIn profile or résumé.

I hold a Ph.D. in Political Economy from the Stanford Graduate School of Business and a Master’s in Statistics from Stanford University. My research spanned best-arm identification in multi-armed experiments and survey experiments on political behavior. Prior to my current role, I worked on data science problems at Netflix and Tesla.