Principal Applied Scientist, Reinforcement Learning, Supply Chain Optimization Technologies

Are you seeking an environment where you can drive innovation? Do you want to be at the forefront of applying machine learning to solve real world problems? Do you want to play a key role in the future of Amazon's Stores business? Come and join us!

The Supply Chain Optimization Technologies (SCOT) group is seeking a Principal Applied Scientist to join our Reinforcement Learning team. Our research team, which includes Sham Kakade and Dean Foster, has published research in top journals and conferences and has a significant impact on the field. Through the launch of several Deep RL models into production, our work also affects decision making in the real world.

Key job responsibilities
Key job responsibilities include:

- Design, implement and evaluate models, agents and software prototypes
- Technical leadership for a group of highly motivated and talented scientists
- Engage key business stakeholders and scientists to surface opportunities for improvement and identify business requirements.
- Work closely with partner teams to develop solutions to ambiguous business problems and integrate novel methodology into our team and business.
- Work closely with senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community.
- Raise the bar of scientific research by innovating and publishing

About the team
Supply Chain Optimization Technologies (SCOT) owns Amazon’s global inventory planning systems. We decide what, when, where, and how much we should buy to meet Amazon’s business goals and to make our customers happy. We decide how to place and move inventory within Amazon’s fulfillment network. We do this for hundreds of millions of items and hundreds of product lines worth billions of dollars of world-wide. Venturing beyond traditional operations research methods for sequential decision-making in inventory planning, the Reinforcement Learning team is pioneering the application of reinforcement learning techniques for these applications. The team combines empirical research and real world testing, backed by a robust theoretical foundation. Some research publications include:

- Deep Inventory Management [https://arxiv.org/abs/2210.03137, NeurIPS 2022 Workshop Presentation]
- Learning an Inventory Control Policy with General Inventory Arrival Dynamics [https://arxiv.org/abs/2310.17168]
- Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data [https://arxiv.org/abs/2112.07602]
- What are the Statistical Limits of Offline RL with Linear Function Approximation? [https://arxiv.org/abs/2010.11895, NeurIPS 2021 Workshop Presentation]
- A Study on the Calibration of In-context Learning [https://arxiv.org/abs/2312.04021]

We encourage collaboration across teammates and recognizes the need to take chances and try new ideas that may fail. Furthermore, our builder culture means that Scientists and Software Development Engineers work closely together to invent and construct at a massive scale.