Applied Scientist, Supply Chain Optimization Technologies (SCOT) - Specialized Selection
Amazon.com Services LLC is recruiting an Applied Scientist for the Specialized Selection team within Supply Chain Optimization Technologies (SCOT), based in Bellevue, Washington. The official employer posting carries Job ID 10537551, was reported as originally posted on October 15, 2026, and was verified open on October 28, 2026, with an Apply now option available.
The team develops machine learning and optimization systems to determine which products Amazon should offer when warehouse space and assortment capacity are limited. The work covers fast-delivery programs, perishable grocery products and emerging delivery offerings, balancing customer purchase intentions with physical capacity constraints.
According to the job description, the research methods include supervised and unsupervised machine learning, large language models, mixed-integer linear programming, reinforcement learning, causal inference and experimental design. Research questions include product substitutability, complementary products, basket composition, delivery-speed sensitivity, constrained assortment optimization, and supply and demand forecasting.
The posting discloses a Bellevue annual base salary range of USD 142,800 to USD 193,200, with additional sign-on payments and restricted stock units (RSUs) described. No specific contract classification or remote, hybrid or on-site work arrangement is inferred.
Illustration disclosure: the image displayed in the company-logo position is a thematic Pexels stock photograph, not Amazon's official logo or imagery. It does not document an Amazon facility, product selection system or actual deployment and does not imply Amazon's endorsement.
This role makes assortment selection a constrained supply chain decision rather than a pure product-ranking exercise. Amazon must decide which items deserve scarce warehouse space when customers differ in their preferences, substitution behavior and sensitivity to delivery speed. The value of machine learning and optimization depends on expressing the real capacity constraints, understanding substitution effects, and evaluating outcomes in terms of customer service, operational feasibility and economic contribution. A model that maximizes predicted demand may still produce a poor assortment if it ignores capacity, margin or the consequences of missing complementary products. The important question is how scientific recommendations become accountable assortment commitments and which business owners can revise them when operational conditions change. This is a distinct scientific problem from Amazon's recently covered Grocery S&OP labor-forecasting position, but it should be interpreted as part of the company's broader investment in supply chain science, not a separate proven transformation.