Applied Scientist, Selection and Inventory Science

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of building models for business application experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Experience in designing experiments and statistical analysis of results
- Experience in professional software development

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The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Bellevue - 142,800.00 - 193,200.00 USD annually
As an Applied Scientist, you will work on high-impact, real-world problems in product selection and inventory management using techniques from operations research, causal inference, forecasting, reinforcement learning and agentic artificial intelligence. You will develop state-of-the-art solutions to optimize customer experience with supply chain levers. You will partner closely with engineering and business teams to turn these models into trusted, scalable decisions. You will publish your work in top conferences and journals.

Key job responsibilities
- Develop stochastic and multi-objective optimization models for assortment planning.
- Apply causal inference and forecasting to measure impact and inform decisions.
- Build reinforcement learning foundation models for large-scale sequential decision making in inventory management.
- Translate rigorous science into clear, actionable recommendations, and communicate them to stakeholders.