Sr. Manager, Applied Science, Supply Chain Optimization Technologies

Amazon•New York, NY

19d

About The Position

Are you seeking an environment where you can drive innovation? Do you want to be at the forefront of solving the toughest real-world supply chain problems? Do you want to play a key role in the future of Amazon's Stores business? Come and join us! 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 worldwide. Check our website if you are curious to learn more about the breadth of problems we tackle: https://www.amazon.science/tag/supply-chain-optimization-technologies We are seeking a Sr. Manager of Applied Science with expertise in Statistical Machine Learning and/or Reinforcement Learning to drive research, development, and deployment of AI technology that empowers SCOT to build, run, and continuously improve the world's most efficient supply chain. To achieve this goal, we accelerate ML / RL software adoption through our partnerships and infrastructure. In this role, you will manage a team of scientists tasked researching the next generation of solutions to power buying, placement, and fulfillment decisions, while translating customer needs into reusable software infrastructure that accelerates adoption and deployment.

Requirements

Ph.D. in Computer Science, Applied Mathematics, Statistics or a closely-related field.
Hands-on experience building machine learning or optimization models in a business environment.
Demonstrated experience managing a science team for three or more years.
Expertise in modern Python with applications of efficient large-scale data processing in complex systems.

Nice To Haves

Ability to manage and quantify improvement in multiple business areas resulting from business analytics, optimization techniques, and/or statistical modeling.
Prior experience managing senior scientists as well as a successful record of developing junior members from academia/industry to a successful career track in a business environment.
Significant peer-reviewed scientific contributions in premier journals and conferences.
Familiarity with inventory planning concepts - forecasting, planning, optimization, and logistics - gained through work experience or graduate level education.
Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
Proven ability to work effectively in a cross-functional team.

Responsibilities

Lead technical innovation in reinforcement learning applications for complex supply chain environments, solving unique challenges and ensuring practical implementation in real-world deployments.
Build and develop a high-performing team by fostering collaboration, mentoring top talent, and creating a balanced culture of technical excellence.
Bridge technical possibilities with business requirements by providing strategic judgment, evaluating technology feasibility, and managing implementation risk.
Drive cross-functional collaboration by interfacing with product teams, leadership, and partner organizations to translate business needs into technical solutions.