Job Description

Worldwide Global Selling has been helping individuals and businesses increase sales and reach new customers around the globe. Today, more than 50% of Amazon's total unit sales come from third-party selection. The Global Selling team in China is responsible for recruiting local businesses to sell on Amazon's 19+ overseas marketplaces and supporting local Sellers' success and growth on Amazon. Our vision is to be the first choice for all types of Chinese business to go globally.

The Worldwide Global Selling Analytics, Intelligence, and Technology (WWGS-AIT) team serves as the research, automation, and insight arm of the International Seller Service data hub, enabling rapid delivery of growth insights through strategic investments in regional data foundations, self-service business intelligence solutions, and artificial intelligence tools.

The WWGS-AIT team is positioned to establish AI-ready foundational capabilities across the WWGS organization while maintaining excellence in business insight generation, and self-service BI/AI application development.

WWGS-AIT is looking for a Data Scientist to design and build seller-facing AI agents that turn our AI-ready data foundation into intelligent, conversational experiences for Amazon's global sellers. You will own the intelligence layer of these agents end-to-end, from modeling and retrieval to evaluation and launch, working alongside applied scientists, data engineers, and the Seller Assistant platform team to put trustworthy AI directly into sellers' hands.

Key job responsibilities
- Design, build, and iterate seller-facing AI agents (LLM-powered) that help Chinese sellers grow globally, reasoning over WWGS-AIT's AI-ready data foundation and knowledge base.
- Develop the intelligence layer of agents: retrieval-augmented generation (RAG) over our knowledge management system, tool-use / function-calling orchestration, prompt engineering, and model fine-tuning or adaptation where needed.
- Ground agent responses in standardized metrics and unified seller profiles to guarantee consistency and accuracy across agents; design and enforce guardrails that prevent hallucination and protect sensitive, compliance-restricted data.
- Build rigorous evaluation frameworks (golden datasets, offline evaluation, and online experimentation) to measure and continuously improve agent quality, safety, and seller impact.
- Develop seller-intelligence models (segmentation, entity resolution / One-ID, ranking and recommendation) that power personalized agent experiences.
- Partner with WWGS Tech and the Seller Assistant platform team to productionize agents and tools (e.g., via MCP), defining the model and intelligence contract while engineering operates the runtime.
- Collaborate with business, product, and cross-functional partners to translate seller pain points into agent capabilities and measurable business outcomes.
- Stay current with advances in GenAI and agentic systems, and bring applied research into production.

Basic Qualifications

- 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
- Master's 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

Preferred Qualifications

- 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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