ppThe 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. /ppWe 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. /ppOur 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. /ppAs 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 PL, 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. /ppWe 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. /ph3Key job responsibilities /h3ulliTranslate ambiguous business challenges into well-defined scientific problems with measurable impact. /liliIdentify opportunities to improve fee revenue measurement, prediction, planning, structure, and level. /liliIdentify opportunities to improve measurement, and prediction of other items of the PL, at appropriate levels of granularity. /liliDesign, 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 PL. /liliPartner closely with finance and fee strategy teams to formulate scientific questions, communicate results, and productionalize solutions. /liliApply rigorous simulation methods to validate models and quantify business impact at scale. /liliCommunicate 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. /li /ulh3About the team /h3pAmazon’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. /ppOur 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. /ph3Basic Qualifications /h3ulli2+ years of data scientist experience /lili3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab,



etc.) experience /lili3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience /lili1+ years of guiding and coaching a group of researchers experience /lili1+ years of working with or evaluating AI systems experience /lili1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience /liliMaster's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM) /liliExperience applying theoretical models in an applied environment /li /ulh3Preferred Qualifications /h3ulliPh.D. in Science, Technology, Engineering, or Mathematics (STEM) /liliKnowledge of machine learning concepts and their application to reasoning and problem-solving /liliExperience in Python, Perl, or another scripting language /liliExperience in a ML or data scientist role with a large technology company /liliExperience in defining and creating benchmarks for assessing GenAI model performance /liliExperience working on multi-team, cross-disciplinary projects /liliExperience applying quantitative analysis to solve business problems and making data-driven business decisions /liliExperience effectively communicating complex concepts through written and verbal communication /li /ulpOur 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. /ppAmazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. /p /p #J-18808-Ljbffr

📌 Data Scientist, Seller Fee Science (Asti)
🏢 Amazon
📍 Asti