The Info org is broadly accountable for two things at Stripe:1/ The effective use of data for decision-making and automation (e.g., ML Infra, Data Science, Applied ML)2/ Scaling efficient go-to-market motions (e.g., Growth helps millions of companies discover, get started, and grow on Stripe with a fully self-serve experience while GTM Tech facilitates efficient high-touch motions)

With millions of merchants using Stripe’s software and APIs to accept payments and start, run, and scale their businesses, Stripe has near infinite opportunities to harness its unique data to help both Stripe and its merchants better understand and optimize their businesses. Our data scientists, data analysts, and data engineers fuel data-driven decisions and build data-powered solutions.

Led the end-to-end data org at Coursera, a diverse group of data scientists and data engineers united in building a better teaching and learning platform through data-driven decisions and data-powered products.Team spanned three functions:1. Data engineering: building the foundations for efficient execution (e.g., data tooling, core data lake, self-serve analytics), plus in-platform analytical products for Coursera’s university partners and enterprise customers2. Decision science: driving product and business strategy through measurement, experimentation, and causal inference3. Machine learning: building algorithmically-powered learner, educator, & employer-facing products including for content discovery, personalized learning, scaleable teaching, skill measurement, and talent benchmarkingPrevious roles: - Senior Director of Data Science (October 2018 - February 2020)- Director of Data Science (May 2017 - September 2018)- Data Science Manager (September 2014 - April 2017)- Data Scientist (March 2014 - August 2014)

Global Future Council on the New Agenda for Education and Skills (2020-2021)Global Future Council on the New Education and Work Agenda (2019-2020)Global Future Council on New Metrics (2018-2019)

Fields: Labor Economics, Public Economics Thesis: Essays in Applied Microeconomics. Topics included- Using machine learning to analyze network effects in consumer decision making - Field experiments in an online labor market quantifying why and how job referrals matter Each published in a Top-5 Economics journal.Advisors: Claudia Goldin and Larry KatzHonors and awards: - Harvard Presidential Scholar, 2009 - 2014- Presidential Fellow Dissertation Completion Award, 2013 - 2014 - Lab for Economic Policy and Applications Research Grant, 2012 - 2013- Bok Center for Teaching and Learning Distinction in Teaching, 2011 - 2013- Simon Kuznets Research Grant, 2011 - Rita Ricardo-Campbell Fellow in Economics, 2010 - 2011A.M. in 2012

- Five semesters as teaching fellow for undergraduate and graduate courses in public, education, and labor economics at Harvard, including two semesters guiding seminars in original research. Developed section material and exam content, and curricula on the empirical research process. Courses led by Professors Larry Katz, Claudia Goldin, Marty Feldstein, Jeff Liebman, and Larry Summers. - Five-time recipient of the Bok Center for Teaching and Learning Distinction in Teaching.

- The Phi Beta Kappa Student Prize ('to the student of highest academic standing at graduation'), 2009- The Halbert White ’72 Prize ('to the most outstanding Economics major', shared with H. Staude), 2009- The Wolf Balleisen Memorial Prize ('for the best senior thesis in Economics'), 2009- summa cum laude, 2009- Early-Selection Phi Beta Kappa, 2008- Shapiro Prizes for Academic Excellence, 2006 and 2007Thesis: An Integrated Economic Analysis of Discrimination in American Theater

Investigated the market for Biotech-Pharma partnerships: quantified stated and revealed preferences in partner selection; modeled pipeline advancement; identified variation in deal structure and terms across product and partner types; performed network analysis of partnering activities.