PhD Economics Intern – Reduced Form Causal Inference, Summer 2027

Saved job description

Amazon Economics is seeking Reduced Form Causal Analysis (RFCA) Economist Interns who are passionate about applying econometric methods to solve real-world business challenges. RFCA represents the largest group of economists at Amazon, and these core econometric methods are fundamental to economic analysis across the company. In this a full-time internship (40 hours per week, with hourly compensation). You'll work with large-scale datasets to analyze causal relationships and inform strategic business decisions, gaining hands-on experience that's directly applicable to dissertation writing and future career placement.

By applying to this role, you are automatically being considered for all our available RFCA internships in 2027.

Key job responsibilities

As an RFCA Economist Intern, you'll specialize in econometric analysis to determine causal relationships in complex business environments. Your responsibilities include:

- Analyze large-scale datasets using advanced econometric techniques to solve complex business challenges

- Applying econometric techniques such as regression analysis, binary variable models, cross-section and panel data analysis, instrumental variables, and treatment effects estimation

- Utilizing advanced methods including differences-in-differences, propensity score matching, synthetic controls, and experimental design

- Building datasets and performing data analysis at scale

- Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations

- Tackling diverse challenges including program evaluation, elasticity estimation, customer behavior analysis, and predictive modeling that accounts for seasonality and time trends

- Build and refine comprehensive datasets for in-depth economic analysis

- Present complex analytical findings to business leaders and stakeholders

Requirements: - 3rd year or greater PhD student in Economics or related field