New York
I work at the intersection of optimization and machine learning, building large-scale models to make decisions under uncertainty of key inputs like demand or costs. Right now that means pricing for a two-sided marketplace at Lyft.
Before Lyft I was a Research Scientist in Modeling and Optimization (MOP) at Amazon in Seattle, working on middle-mile and inventory optimization. I did my PhD in Operations Research at MIT with Georgia Perakis, working on supply chain projects with companies like Inditex, IBM and Flipkart, as well as COVID-19 forecasting for the CDC.
Lately I've been exploring how to integrate LLMs and agents to improve efficiencies in both business decisions and research.
Experience
Tech Lead, Marketplace Pricing
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Staff Data Scientist Jul 2026 — Present
Building granular, dynamic pricing portfolios across multiple regions and ride modes.
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Senior Data Scientist Apr 2025 — Jun 2026
Developing models to automate and optimize pricing decisions across the marketplace.
Modeling and Optimization (MOP)
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Research Scientist Sep 2022 — Mar 2025
Leveraging optimization techniques and data science to improve the speed and scalability of Amazon's fulfillment operations while reducing supply chain costs.
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Research Intern Summer 2020
Developing algorithms and models that integrate prediction and prescription for revenue management applications.
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Research Extern Jan — Feb 2020
Novel machine learning methods to improve demand forecasting for new products.
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Research Assistant Sep 2017 — Aug 2022
Operations Research Center.
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Teaching Assistant Jan 2019 — May 2021
Executive MBA Class 20', 21', 22'.
Education
PhD, Operations Research · Operations Research Center
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Dissertation
Constrained Inventory Optimization on Complex Warehouse Networks.
Advisor: Prof. Georgia Perakis.
Diploma (MEng), Computer Science · School of Electrical and Computer Engineering
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Diploma Thesis
Algorithms for Multidimensional Load Balancing.
Advisors: Prof. Dimitris Fotakis (NTUA), Prof. Paris Koutris (UW–Madison).