The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers worldwide. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers.
We are looking for an experienced Applied Scientist to improve our understanding of seller economics and reduce defects in our fee charging processes worldwide. In this role you will develop large scale prediction and classification models to influence the fee calculations that impact billions of products listed by third-party sellers. You will collaborate with other Applied Scientists, Economists, Software Developers, and Product Managers.
Key job responsibilities
· Translate ambiguous fee related business problems into rigorous scientific models.
· Develop large scale classification and/or prediction models using state-of-the-art techniques to further our understanding of third-party seller economics.
· Write high quality code and implement scalable models within the production systems.
· Stay up to date with relevant scientific publications.
· Collaborate with business and software teams both within and outside of the fees organization.
· M.S. or PhD degree in Statistics, Machine Learning, Operations Research, Computer Science, or similar field.
· 3+ years of experience developing machine learning models in a professional environment.
· Hands on experience with scripting languages such as Python
· Sound theoretical understanding of broad machine learning concepts, with deep and demonstrable expertise in at least one topic or application of machine learning.
· PhD in Statistics, Machine Learning, Operations Research, Computer Science, or similar field.
· Expertise across deep learning techniques applied to large scale classification problems.
· Experience developing and deploying models with cloud computing technology.
· Superior verbal and written communication, ability to convey rigorous mathematical concepts to technical and non-technical audiences.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.