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Summary

Ph.D. candidate in Economics at the University of Texas at Austin with an M.S. in Statistics. Specialize in causal inference, pricing, demand estimation, and structural modeling using large-scale observational data. Experience spans production software engineering, antitrust litigation, policy research and machine learning.

Education

• University of Texas at Austin	Austin, TX
  Ph.D. Economics (expected 2026); M.A. Economics	Aug 2020–Present

• University of Arizona	Tucson, AZ
  M.S. Statistics; B.A. Mathematics and Computer Science	Aug 2013–May 2018

Skills

• Programming: Python, R, Julia, SQL, TypeScript, Rust, Java, C, Stata, MATLAB, Bash

• Economics & Statistics: Causal inference, structural modeling, demand estimation, machine learning (XGBoost), nonparametrics, numerical optimization

• Tools: Pandas, tidyverse, Postgres/PostGIS, spatial analysis, Docker, Git, HPC, Make/Snakemake, LaTeX

• Languages: English (native); Spanish (proficient)

Professional Experience

• Texas Office of the Attorney General	Austin, TX
  Economics Fellow	Jul 2024–Jun 2025

  • Analyzed antitrust cases involving algorithmic collusion, online advertising auctions, and digital markets using industrial organization methods.

  • Drafted economic analyses and evaluated expert reports to support litigation strategy in complex antitrust cases.

  • Presented analyses to attorneys and translated complex quantitative methods into clear, actionable legal arguments.

• World Bank Group	Remote
  Short-Term Consultant	Feb 2021–Aug 2021

  • Developed statistical indices and quantitative metrics measuring political participation in Afghanistan, using large-scale survey data.

  • Developed algorithms for ordinal survey aggregation and PCA-based index construction.

  • Authored technical documentation and communicated statistical methods and findings to economists, policy researchers, and other stakeholders.

  • Developed and led internal training on R, tidy data, functional programming, and reproducible workflows.

• JPMorgan Chase & Co.	Houston, TX
  Software Engineer	Jun 2018–Jun 2020

  • Developed and maintained production trading systems in Python and TypeScript supporting interest rate derivatives.

  • Designed regulatory workflows and trade models for new financial instruments.

  • Migrated legacy platform to microservice architecture using React, GraphQL, and WebSockets.

  • Collaborated with traders, product managers, engineers, and quantitative researchers to prioritize requirements and deliver production software.

Selected Research

• Dealer Consolidation and Vertical Relationships: Causal analysis of automobile dealership consolidation using data on millions of car sales and geospatial analysis to estimate effects on prices and product availability.

• Dynamic Pricing in Automotives: Developed and estimated a dynamic structural model of manufacturer-dealer relationships. Combined structural estimation with machine learning (XGBoost) to recover underlying allocation and sales target policy of the manufacturer. Used estimates to recover welfare effects of dealer allocation policy.

• Demand Elasticity and Curvature (with Eugenio Miravete): Nonparametric estimation of consumer demand to study the relationship between elasticity, curvature, pass-through, and market power. Contrasted nonparametric estimates with common parametric demand families and showed important implications for estimation and competition policy.

Additional Experience

• Teaching: Teaching assistant for Master’s Econometrics, Industrial Organization, and Causal Inference. Led weekly review sessions, designed programming assignments, and taught statistical computing.

• Research Computing: Led workshops on high-performance computing and reproducible research for Economics Ph.D. students. Developed departmental guidance for parallel computing in Julia, Python, and MATLAB.