Dinesh R Poddaturi, Ph.D.
I am an applied economist working at the intersection of pricing, industrial organization, and experimentation. I use demand estimation, causal inference, and counterfactual analysis to address economic decision problems, from marketplace pricing strategy to competition and merger analysis.
I currently work as an economist at Digonex, a dynamic pricing company. In my previous tenure there, I spent over two years leading pricing strategy across 20+ client engagements in the U.S., U.K., and Canada. That work spanned end-to-end pricing systems, from estimating demand elasticities using transaction data and building revenue optimization frameworks to designing A/B tests and translating findings into executive strategy and pricing decisions. The work generated $27M in cumulative incremental client 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 evaluate potential price effects and consumer welfare impacts under the model's assumptions. I have also built an open-source switchback experiment framework that simulates a delivery-fee test in a three-sided marketplace and examines how supply-side spillovers from courier migration bias naive treatment-effect estimates.
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 simulation, antitrust economics, marketplace economics, experimental design, A/B testing, switchback experiments, econometrics.
Tools: Python, R, SQL, Stata.
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, and to relocation for the right opportunity.
