I am a data scientist in the Experimentation Team at Canva, where we use a Bayesian framework to conduct experiments. I'm responsible for developing the statistical models behind our experimentation platform; for shaping tools and flows within our platform to offer a fast, reliable and data-driven decision making process for the release of new features; and for advising on the right causal inference techniques to address a broad range of business needs when simple AB testing is not available.
Prior to Canva, I worked in the Eater Promotions team at Uber Eats, where we used ML causal techniques to make inferences from large-scale platform data in order to target the right customers with the right incentives.
Prior to my career in tech, I completed my PhD in Economics at Harvard University, where I taught causal inference and econometrics at the undergraduate and graduate levels (teaching materials for the graduate course are available on the left sidebar). My published research spans the areas of climate econometrics and health economics.