Specializing in structural econometrics, causal inference, and machine learning to solve high-stakes workforce challenges at scale. Leading teams to deliver data-driven insights that shape strategic decisions for hundreds of thousands of employees.
Combining rigorous econometric methodology with practical business applications to drive measurable impact
Lead scientists delivering structural econometric models and causal inference analyses that inform critical workforce decisions affecting hundreds of thousands of employees. Work cross-functionally with senior leadership, product managers, engineers, and data scientists to productionalize science at scale.
Built a discrete choice econometric model estimating labor supply elasticities for employees across locations. The model combines internal and external data sources to quantify workforce response to wage and amenity changes. Used by senior leadership to inform compensation strategy, enabling counterfactual analysis of inflation scenarios, attrition patterns, and policy changes.
Led development of a causal predictive model for candidate screening that is in production and used at scale. Continuously iterated on model accuracy, driving alignment with senior leadership. Generated economically impactful savings through improved hiring efficiency and reduced turnover.
Developed causal predictive framework to identify key drivers of employee experience, securing strong executive buy-in. Model outputs directly inform operational decisions across the network, combining traditional econometric methods with modern ML techniques including GenAI-powered text embeddings for feature extraction.
Conducted rigorous research in labor economics, public economics, and economics of education. Published in top-tier journals including Journal of Labor Economics and Journal of Public Economics. Developed expertise in structural modeling, causal inference methods, and computational economics that now underpins my industry work.
Explore key econometric concepts through interactive visualizations
Explore how labor supply and demand determine market-clearing wages. Hover over wage levels to see workforce supply, demand, and any excess supply or demand.
Explore confounding and causal identification through an interactive DAG. Toggle variables on/off to see how causal paths change and whether treatment effects can be identified.
Select any topic below to learn about key econometric methods and concepts.
Research foundation in applied econometrics and causal inference