Culver City, California, United States
I am a Quantitative Economist in training, specializing in experimentation and applied machine learning for marketplace dynamics. I've graduated from the University of California, Santa Cruz, with a Master of Science in Quantitative Economics. Throughout my graduate study, I took courses in Consumer Welfare Theory, Applied Econometrics, Machine Learning, and Numerical Optimization. I have joined Amazon's Ads Economics team in Los Angeles as an Economist Apprentice, where I will contribute to research in consumer welfare and pricing science through experimentation and empirical analysis.
‣ Empirical IO and causal inference in support of advertising measurement and pricing science.
‣ Designed and deployed a demand forecasting model using Gradient Boosting to generate monthly 3-month-ahead predictions, applying structured model validation to assess out-of-sample performance across high-variance shipment locations. ‣ Conducted large-scale exploratory analysis of two years of transactional data across three product classes, identifying seasonal patterns and distributional anomalies that drove model selection and reduced walk-forward forecasting error by 15%. ‣ Developed validation dashboards surfacing forecast accuracy by revenue segment, identifying a $10M segment with >90% historical accuracy and translating findings into procurement recommendations for operations stakeholders.
‣ Analyzed high-demand product data to identify gaps in data completeness, prioritizing additional inputs to improve decision-making reliability for Finance and BI teams. ‣ Built automated SQL pipelines to extract, transform, and load financial data into a structured source, enabling reproducible aggregation and presentation for the Income Statement dashboard. ‣ Developed a financial reporting dashboard in Tableau that matched official reporting figures, eliminating ~$60,000/yr in third-party software costs and initiating a department-wide migration. ‣ Presented a proof-of-concept analysis to management that informed a reporting infrastructure decision affecting both Finance and BI teams organization-wide.