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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.
Our 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.
As an data scientist on our team, this role will focus 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. This blends the tools of data science, statistics, and ML/AI. Your work will shape not only how fees are decided, but how they are interpreted and planned.
We are seeking scientists who are motivated by first principles, disciplined experimentation, and the technical challenge of deploying ideas at global scale. This is an opportunity to work on consequential problems where analytic rigor meets real-world complexity, and where your analysis, models, algorithms, and systems will directly influence the experience of millions of sellers. If you are driven to build elegant solutions to hard problems-and to see them operate in production at meaningful scale-we would welcome the opportunity to build with you.
Key job responsibilities
.. 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.
About the team
Amazon's third-party marketplace is a multibillion-dollar global service, connecting customers and sellers across through billions of transactions annually. The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide business fee strategy, ensure fees are accurately computed for millions of products, and improve the seller experience with AI tools that support any fee related contact (understanding, audit, and dispute). We build the scientific foundation that empowers sellers to grow their businesses with clarity and confidence.
Our 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.
- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- 1+ years of guiding and coaching a group of researchers experience
- 1+ years of working with or evaluating AI systems experience
- 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
- Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (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 ML or data scientist role with a large technology company
- Experience in 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 making data-driven business decisions
- Experience effectively communicating complex concepts through written and verbal communication
Our inclusive culture empowers Amazonians to deliver the best results for our customers. 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 for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
Job ID: 149960389
Skills:
Machine Learning, Data Analytics, risk management, Process Management, Fraud Detection, Ai, project management
Skills:
data engineering , Cloud deployment, Machine Learning, Personalization, Predictive Analytics, Python, Computer Vision, Time-Series Analysis, Recommendation, Optimization, anomaly detection, Statistical Modeling
Skills:
Data Modeling, XGBoost, Machine Learning Algorithms, Python, Sql, Random Forest, Machine Learning Frameworks, Cloud Deployment on AWS
Skills:
Data Analytics, Machine Learning, risk management, Process Management, project management, Ai, Fraud Detection
Skills:
Alteryx, Tableau, Tensorflow, Data Science, React, Typescript, Machine Learning Algorithms, Python, Google Cloud Platform, Hadoop, Power Bi, Qlikview, Sql, Pytorch, Spark, Keras, FastAPI, Qliksense, Statistical Libraries, Tekton, Gemini APIs, ETL Processes, Cloud Run, R, Vertex AI, Google Cloud Build, Cloud Functions, Google Kubernetes Engine
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