Amazon Applied Scientist Seller Fees Science & Tech 

United States, Washington, Seattle 

191892321

We are looking for an Applied Scientist to join us on improving accuracy in billion-scale fee transactions worldwide and enhancing selling experience with fee policies and services. In this role you will develop large scale machine learning models to drive the fee calculations that impact hundreds of millions of products from third-party sellers in the Amazon product catalog. You will leverage sophisticated statistical methods, supervised and unsupervised learning models, as well as generative AI that can scale to production requirements worldwide. You will participate in developing models at the intersection of deep learning and causal inference. You will collaborate with other Applied Scientists, Research Scientists, Data Scientists, Economists, Software Developers, and Product Managers.Key job responsibilities
Responsibilities:
. Design measurable and scalable science solutions that can be adapted across stores worldwide with different languages, policy and requirements.
· Develop large scale classification and prediction models using the rich features of text, image and customer interactions and state-of-the-art techniques.
· Research and implement novel machine learning, statistical and econometrics approaches.
· Write high quality code and implement scalable models within the production systems.
· Stay up to date with relevant scientific publications.

- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- Advanced degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Experience building machine learning models or developing algorithms for business application
- Experience researching about machine learning, deep learning, NLP, computer vision, data science

- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
- Experience with theory and practice of design of experiments and statistical analysis of results