About

I’m a Senior Staff Data Scientist at Patreon, where I help drive product and business decisions through experimentation, causal inference, and analytics. Prior to Patreon, I held data science leadership roles at TikTok and Meta. My academic background is in economics—I earned a PhD from Rice University with a focus on industrial organization, marketing, and applied econometrics. I enjoy working at the intersection of rigorous quantitative methods and real-world product challenges.

My job market paper estimates the correlation of household advertising elasticities across various brands of chocolate and laundry detergent. It extends the dynamic panel methods of Arellano and Bond (1991) to allow time-varying random coefficients that can be correlated with regressors (advertising exposure) and correlated across equations in a SUR (seemingly unrelated regressions) system.

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