I lead Applied Science at Glassdoor, where my team leverages advanced methods in econometrics, machine learning, optimization, testing, etc. along with a deep empathy for our stakeholders to drive impact for the business. Our key areas of interest are engagement and retention models as well as causal models measuring the value of our products.
I have a PhD in Public Policy from Harvard, where my research applied econometrics, statistics, and machine learning to data from Internet platforms. My dissertation Algorithms and Applied Econometrics in the Digital Economy included algorithmic work on nowcasting and dynamic pricing as well as applied econometric research on the value of financial assistance in online learning.
I am passionate about making quantitative methods feel intuitive and accessible to people of all backgrounds. This shows up in my daily work, but also in more formal teaching engagements. Most recently, I was an Adjunct Lecturer at Harvard, teaching the Masters in Public Policy first year course on data, econometrics, and machine learning (API 202: Empirical Methods II).
My background is highly interdisciplinary. My undergraduate degree is in French and Economics from Princeton and my career has woven through consulting, economic research, and data / applied science. If you would like to connect about a consulting or speaking engagement, please reach out through the Contact form.