Senior Applied Scientist, FBA AI Science & Analytics

Job description

FBA AI Science and Analytics accelerates the AI-native transformation of Fulfillment by Amazon by building, integrating, and scaling AI-powered data & science products and seller-facing experiences that drive operational efficiency and growth across Fulfillment by Amazon globally. We learn seller behaviors, design the policies and incentives that shape their experience, and ship science products that help third-party sellers grow topline and cut operating costs at Amazon scale.

Our work sits at the intersection of machine learning, statistics, economics, operations research, and GenAI/LLMs.

We're looking for a Senior Applied Scientist who wants to put GenAI to work on a hard, high-visibility problem: building next-generation multi-agent systems that interact with millions of sellers and guide them through their toughest challenges at scale. You'll own solutions spanning supervised and unsupervised learning, recommendation systems, statistical learning, LLMs, harness engineering, and reinforcement learning.

The ambition is to make AI a native layer in every seller decision rather than a separate tool sellers must adopt, delivering actionable insight in minutes, not days. You'll shape end-to-end experiences across the highest-frequency seller workflows, including inventory optimization, inbound efficiency, defect improvements, reimbursements, and capacity planning. The role carries direct visibility with senior Amazon business leaders and works together with fellow scientists, engineers, and product teams to launch production-grade agentic capabilities.

Key job responsibilities

- Design, build and deploy FBA’s GenAI architectures end to end.

- Apply state-of-the-art ML and GenAI solve diverse business problems across seller supply chain systems.

- Define the team’s long-term science vision and roadmap, driven fundamentally from our customers' needs, translating those directions into specific plans for scientists, engineers, and product partners.

- Partner closely with scientists and software engineers to drive real-time model implementations and deliver high-impact features.

- Establish scalable, efficient, automated processes for large scale data analyses, model benchmarking, model evaluation and model implementation.

- Advocate the right ML solutions to business stakeholders, engineering teams, as well as executive level decision makers