Applied Scientist, Seller Fee Science

- 5+ years of building machine learning models for business application experience
- PhD, or Master's degree and 5+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience in designing experiments and statistical analysis of results
- Experience building machine learning models or developing algorithms for business application

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Amazon’s third-party marketplace is a multibillion-dollar global ecosystem, connecting customers and sellers across the world through millions 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 improves 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. For example, measurement of item dimensions (what are the dimensions of a bag of apples?) , large-scale simulation of policy changes (how do marketplace dynamics change when...), Leveraging AI to simplify/document fee policy, resolve disputes, and provide detailed fee explanations to our sellers (explain how this fee is computed and what can be done to reduce costs).

As a Senior Applied Scientist on our team, this role will lead the application of machine learning and artificial intelligence to predict and reconcile measurement of products globally. This blends together statistical modeling, application of NLP, image processing, classical machine learning, cost-benefit analysis, causal modeling, and optimization. You will partner closely with engineers and product partners to take your solutions from research to production. You will also help to set the team direction, influence partner teams across product and engineering, help establish a strong scientific culture within the team (e.g., publication, seminars, etc.) and grow junior scientists.

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 mathematical rigor meets real-world complexity, and where your 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
* Identify opportunities to improve Seller Experience and translate ambiguous business challenges into well-defined scientific problems with measurable impact.
* Design, develop, and deploy AI/ML models that improve fee accuracy, automate policy-to-code translation, and enhance seller understanding of fee calculations.
* Partner closely with engineering and product teams to productionize solutions, meeting latency, scalability, reliability, and other system constraints.
* Apply rigorous experimentation, causal inference, and 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.
* Build Team Scientific Culture and scientific Standards
* Grow and Develop Scientific Talent on the team