I am an economist and data scientist. In my professional work, I use economics, statistics, and computer science to build systems that inform high-stakes business decisions. I have worked at technology companies of all sizes, from Amazon (mega cap), to Netflix (large cap), to Bird (late stage to IPO), to Hush (early stage to acquisition). I’m currently the VP of Machine Learning at Rappi.

At Amazon, I used causal econometrics to determine how Amazon Prime members were attributed and how investments were allocated across Amazon's various businesses like retail, video, music, and hardware devices. At Netflix, I used machine learning to engineer statistical tools that helped decide which titles to license, which scripts to make into shows, and which talent to sign. At Hush, I built from scratch a data organization that spans all three areas of data analytics, data engineering, and data science. At Bird, I led a team of data scientists to execute on data products fundamental to the core of the business, including vehicle allocation, marketplace management, experimentation, and dynamic pricing.

Academically, I hold a Ph.D. in Economics from Stanford University, where I spent 5 years conducting research in theoretical statistics, studying under the tutelage of the Nobel laureate Guido Imbens. Since 2017, I have served as Adjunct Professor of Economics at the University of Southern California, where I direct the Big Data Econometrics track of the Master’s program of the Economics department. Each year I also teach a graduate course titled Machine Learning and Causal Inference.

Other than work, my hobbies include photography, tennis, and skiing. Through this website I hope to share some of the images and software I have created. Separately, you can find an Instagram account that features more of my photography work, a GitHub page that hosts source code of the various software I have developed, and a YouTube channel that has some weird videos.