Sr. Data Scientist , Worldwide Global Selling AIT

Worldwide Global Selling has been helping individuals and businesses increase sales and reach new customers around the globe. Today more than 50% of Amazons total unit sales come from third-party selection. The Global Selling team in China is responsible for recruiting local businesses to sell on Amazons 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 Senior Data Scientist to build reusable science capabilities that support seller growth operational decision-making and cross-domain innovation across Worldwide Global Selling.

You will lead high-impact modeling initiatives at the intersection of graph science machine learning simulation and optimization. Your initial focus will include building identity-resolution and entity-linkage capabilities that create a trusted One ID view across fragmented seller and business data; developing simulation and decision models for logistics and inventory options; and establishing reusable modeling foundations for seller lifecycle and other cross-domain use cases.

You will also partner with business product engineering and analytics teams to evaluate and deliver prioritized science opportunities through a common intake process. This role is ideal for a hands-on scientist who can move from ambiguous business problems to robust production-ready models and decision systems.

Key job responsibilities
- Lead the design development and productionization of graph-based identity-resolution and entity-linkage models that connect seller account business logistics and other relevant entities into a trustedOne IDfoundation.
- Develop simulation optimization forecasting and decision-support models for logistics inventory and related operational choices; quantify trade-offs uncertainty and expected business impact.
- Establish scalable model-development practices including feature engineering experiment design model validation monitoring reproducibility documentation and responsible-use controls.
- Translate ambiguous business questions into clear science problems measurable hypotheses model requirements and decision frameworks.
- Partner with Data Engineering BIE Product and domain teams to build reliable data pipelines model features evaluation datasets and production model interfaces.
- Support prioritized science needs from Supply Chain Seller Success and other teams through the WWGS-AIT operating-planning intake and prioritization process.
- Define model performance business-impact and operational-success metrics; use offline evaluation back-testing simulation and controlled experiments to continuously improve solutions.
- Contribute applied AI and GenAI expertise where it improves science-enabled productsfor example model evaluation retrieval/ranking intelligent decision support or AI-agent capabilities grounded in trusted data and models.
- Influence the WWGS science roadmap by identifying opportunities to convert repeated business problems into durable reusable data and modeling capabilities.

- 5 years of data querying languages (e.g. SQL) scripting languages (e.g. Python) or statistical/mathematical software (e.g. R SAS Matlab etc.) experience
- 5 years of data scientist experience
- Experience with statistical models e.g. multinomial logistic regression
- Masters degree in econometrics statistics industrial engineering operations research optimization data mining analytics or equivalent quantitative field

- Experience in e-commerce
- Hands-on experience developing LLM / GenAI applications (e.g. RAG prompt engineering fine-tuning or agent / tool-use frameworks).
- Experience building conversational or agentic AI systems including multi-agent orchestration function-calling or MCP-based tool integration.

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