Dinesh R Poddaturi, Ph.D.
I am an applied economist working at the intersection of industrial organization, pricing, and empirical analysis, with hands-on experience running dynamic pricing in practice. I use demand estimation, causal inference, and counterfactual analysis to answer pricing and competition questions, from marketplace pricing strategy to merger analysis.
For over two years I worked as an economist at Digonex, a pricing optimization consultancy, leading pricing strategy across 20+ client engagements in the U.S., U.K., and Canada. That work spanned end-to-end pricing systems from demand estimation to implementation: estimating demand elasticities from transaction data, building revenue optimization frameworks, designing and executing A/B tests, and translating findings into executive strategy. Total client impact: $27M in incremental revenue.
My research focuses on pricing, demand estimation, and competition economics. One working paper examines why revenue maximization and profit maximization diverge in elastic markets. It integrates demand estimation, cost sensitivity analysis, customer lifetime value modeling, and an implementation framework across 110,840 e-commerce transactions. A second develops an independent structural analysis of the blocked JetBlue-Spirit merger, using a constructed market-level panel calibrated to public case and government data. It combines a BLP random-coefficients demand model with a Bertrand-Nash merger simulation to estimate modeled price effects and consumer welfare impacts.
I enjoy solving problems where economics, data, and implementation intersect. Whether designing pricing systems, evaluating competitive effects, or estimating marketplace behavior, I am interested in translating rigorous economic analysis into decisions that work in practice.
I regularly write about applied economics, causal inference, pricing strategy, industrial organization, and marketplace economics. Writing is available on the blog and on LinkedIn.
Methods: Industrial organization, causal inference, demand estimation, dynamic pricing, discrete choice models, counterfactual analysis, merger and antitrust analysis, marketplace economics, experimental design, econometrics.
Tools: Python, R, SQL.
Academic training: PhD Economics, Industrial Organization (Iowa State University), MS Statistics, MS Information Systems.
I am interested in economist and applied scientist roles in economic consulting and technology, particularly those involving competition, marketplaces, and pricing. Based in Indianapolis, I am open to Chicago-based, remote, and Indianapolis roles.
