Data Scientist, Seller Fee Science

Overview

The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide fee strategy, quantify its impact, and ensure fees are accurately computed and explained for billions of transactions between Amazon selling partners and customers.

We help build the foundations for growing selling partner businesses, bringing the best selection and prices to Amazon customers, and helping Amazon leaders make and implement high‑impact decisions that optimally balance profitability and growth.

The team brings together world‑class economists, physicists, mathematicians, and computer scientists to tackle diverse, challenging problems that require theoretical rigor and deliver real‑world impact. Our work focuses on the application of data analysis, econometrics, machine learning, and artificial intelligence to measure and predict Amazon's P&L, with emphasis on fee revenue. As a data scientist on this team, your analysis, models, algorithms, and systems will shape how fees are decided, interpreted, and planned, and will directly influence the experience of millions of sellers.

• Translate ambiguous business challenges into well‑defined scientific problems with measurable impact.
• Identify opportunities to improve fee revenue measurement, prediction, planning, structure, and level.
• Identify opportunities to improve measurement and prediction of other items of the P&L, at appropriate levels of granularity.
• Design, develop, and deploy econometric or AI/ML models that improve our understanding of the relationship between fees and costs, or predict fee revenue and other elements of the P&L.
• Partner closely with finance and fee strategy teams to formulate scientific questions, communicate results, and productionalize solutions.
• Apply rigorous simulation methods to validate models and quantify business impact at scale.
• Communicate scientific innovations and results clearly to cross‑functional stakeholders and contribute to the broader internal and external scientific community through publications, talks, and technical artifacts.

• 2+ years of data scientist experience.
• 3+ years of experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical/mathematical software (e.g., R, SAS, MATLAB).
• 3+ years of experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance.
• 1+ year of experience guiding and coaching a group of researchers.
• 1+ year of experience working with or evaluating AI systems.
• 1+ year of experience creating or contributing to mathematical textbooks, research papers, or educational content.
• Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in STEM.
• Experience applying theoretical models in an applied environment.

• Ph.D. in Science, Technology, Engineering, or Mathematics (STEM).
• Knowledge of machine learning concepts and their application to reasoning and problem‑solving.
• Experience in Python, Perl, or another scripting language.
• Experience in a machine learning or data scientist role with a large technology company.
• Experience defining and creating benchmarks for assessing GenAI model performance.
• Experience working on multi‑team, cross‑disciplinary projects.
• Experience applying quantitative analysis to solve business problems and make data‑driven business decisions.
• Experience effectively communicating complex concepts through written and verbal communication.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit (Use the "Apply for this Job" box below). for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.