We're looking for a Research Scientist to join a team that builds the science behind how Amazon supports its 1.5M+ employees - from forecasting demand across HR services to optimizing how work gets routed and assigned in real time. You'll own high-impact operations research and causal inference work that directly reduces costs and improves the employee experience at global scale.

In this role, you will:

- Design and build simulation and optimization models that automate workforce scheduling, hiring, and task assignment across Amazon's HR contact centers and back-office operations
- Develop causal inference frameworks to measure the true impact of policy changes, product launches, and AI-driven initiatives on employee experience and operational efficiency
- Collaborate with senior leaders to translate complex analytical findings into actionable strategies that influence staffing, routing, and resource allocation decisions affecting thousands of associates
- Push the boundaries of what's possible by combining operations research with machine learning and generative AI to solve novel workforce optimization problems

This isn't a role where you'll be running standard analyses on repeat. You'll be building novel solutions - like contact center simulators that replicate real-world operations, scheduling optimizers that replace manual processes, and experiment frameworks that credibly quantify the impact of large-scale organizational changes. Your work will directly inform decisions made by VP and Director-level leaders who set the strategy for how Amazon supports its employees at scale.

About the team
We are a team of economists, research scientists, and data scientists within Amazon's People Experience and Technology Finance organization. Our mission is to make Amazon's employee support services smarter, faster, and more efficient through rigorous science - from forecasting models that drive hiring plans to simulation engines that optimize how HR services are delivered to Amazon's global workforce of 1.5M+ people.

Our work runs in production, informs weekly planning decisions, and shapes resource allocation at scale. We value intellectual rigor, ownership, and collaboration — publishing at internal science conferences, maintaining shared codebases, and holding each other to high technical standards. You'll have real ownership from day one: framing problems, building solutions, and presenting results to senior leaders who act on your recommendations.