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Applied Scientist, Long Term Planning & Forecasting

Amazon

Applied Scientist, Long Term Planning & Forecasting

full-timePosted: Aug 12, 2026Updated: Aug 27, 2026Bellevue, Washington, United States

Job Description

Are you passionate about shaping the future of Amazon's long-term success with Generative AI and Reinforcement Learning (RL)? We are looking for a strong Applied Scientist to join Long-Term Planning and Forecasting (LTPF) within Supply Chain Optimization Technologies (SCOT). Our mission is to accelerate the Amazon flywheel and ensure Amazon's long-term success by leveraging GenAI and RL to build the forecasting, optimization, and analytics products that plan Amazon's Topline, Inventory, and Fulfillment Capacity and Topology. In this role, you will develop our next-generation forecasting, planning and insight generation products powered by GenAI and reinforcement learning. You will design and implement novel, sophisticated scientific solutions to large-scale problems that directly shape how Amazon plans for the years ahead. You will collaborate closely with applied scientists, research scientists, economists, product managers, and software engineers. We are seeking innovative thinkers who can balance theoretical rigor with practical implementation. Your ability to communicate complex technical concepts and drive data-driven decisions at massive scale will be crucial to your success.

Locations

  • Bellevue, Washington, United States
  • New York, New York, United States

Salary

172,400 - 223,400 USD / yearly

Skills Required

  • Machine Learningintermediate
  • evaluatingintermediate

Required Qualifications

  • 3+ years of building models for business application experience (experience, 3 years)
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience (experience, 4 years)
  • Experience programming in Java, C++, Python or related language (experience)

Preferred Qualifications

  • Experience developing, deploying and managing AI products at scale (experience)
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience designing or architecting (design patterns, reliability and scaling) of new and existing systems (experience)
  • Familiarity with evaluating and continuously improving GenAI/agentic systems in production (experience)

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