Economist, Amazon Devices Demand Planning

Key job responsibilities

• Design, estimate, and scale Berry-Levinsohn-Pakes (BLP) random coefficients demand models to quantify consumer heterogeneity, own- and cross-price elasticities, and substitution patterns across large product markets.
• Implement and optimize numerical routines—including GMM estimation, contraction mappings, and simulation-based inversion—to solve structural demand systems at scale in Python.
• Develop and validate instrumental variables strategies to address price endogeneity in differentiated product markets, ensuring unbiased and robust demand parameter estimates.
• Build production-grade pipelines that ingest large-scale observational datasets, estimate consumer preferences, and generate product-level demand forecasts on recurring schedules.
• Collaborate with cross-functional teams including product management, marketing, and operations to translate structural model outputs—such as willingness-to-pay and competitive diversion ratios—into actionable pricing and portfolio strategies.
• Advance the team's structural modeling capabilities by researching and deploying extensions to classical BLP frameworks (e.g., supply-side estimation, dynamic demand, micro-moments) and documenting approaches in clear technical reports.

Basic Qualifications

• PhD in economics or equivalent

Preferred Qualifications

• 2+ years of industry, consulting, government, or academic research experience
• Knowledge of at least one statistical software package such as R, Stata, Matlab, SAS
• Experience in prediction and forecasting in a research or industrial environment
• Experience with handling of large datasets