Amazon is looking for a talented Postdoctoral Scientist to join our Seller Fees Science & Tech team for a one-year, full-time research position. Postdoctoral Scientists will drive data-driven innovations in the seller fee domain for our EU marketplace.
In this role, you will have the opportunity to:
Leverage economic methods like modeling and impact estimation to address key issues using large, real-world datasets;
Partner with PMs and BAs to identify data and define metrics evaluating business initiatives and making recommendations;
Execute from idea to implementation as an integral part of cross-functional teams;
Thrive in a highly complex and fast pacing environment.
Participate in research activities, including publishing papers, attending conferences, and collaborating with academic institutions to advance the state-of-the-art in relevant fields.
Key job responsibilities
In this role you will:
Build causal inference modeling to evaluate the effects of policy changes, such as fee adjustments and new fee structures, on seller behaviors and business metrics;
Analyze how sellers and key outcomes are impacted by variations in growth strategies;
Design experiments to measure pilot programs and support scaling-up effort;
Synthesize learnings from past policy changes into critical insight to help the business develop new strategies and make science-based decisions.
Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community.
Publish your innovation in top-tier academic venues and hone your presentation skills.
Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise.
About the team
The Fee Science Team works on science, economic, machine learning, and data engineering projects for Selling Partner Services and third-party fees. Our projects include causal modeling and impact of structural changes, models that support fee integrity, business analytics, and a comprehensive set of data sources and pipelines.
