I am an economist applying causal inference and machine learning to business problems in a multi-sided market setting at AWS. I specialize in designing rigorous measurement frameworks—Difference-in-Differences, synthetic control, propensity score weighting, CLV, and transformer-based causal inference framework—to quantify program impact and guide strategic decisions. You can find more details on my LinkedIn profile.

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.