Data Scientist, Seller Fee Science

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 Amazons 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 problemsand to see them operate in production at meaningful scalewe 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
Amazons 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
- Masters 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

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