Applied Scientist, Worldwide Grocery Stores - Data and Science
Amazon.com Services LLC is recruiting an Applied Scientist for the Worldwide Grocery Stores - Data and Science organization, working with the Sales & Operations Planning (S&OP) and Supply Chain Science team in Seattle, Washington. The official employer listing carries Job ID 10530497. Its original posting date has not been established; the position was verified open on October 27, 2026, with an active Apply now option.
According to the job description, the role focuses on developing and deploying demand and labor forecasting models for the Amazon Grocery Network, combining time-series analysis, Bayesian and structural methods, and machine learning. The forecasts inform downstream workforce and capacity decisions. Amazon identifies forecasting errors as a source of staffing inefficiencies, increased operating costs and weaker customer experience.
The scientist will collaborate with engineering partners, product owners and business stakeholders to connect forecasting techniques with operational requirements and measurable outcomes. The Seattle base salary range disclosed in the posting is USD 136,000 to USD 184,000 annually, with additional sign-on payments and restricted stock units (RSUs) described. No remote, hybrid or on-site arrangement is inferred.
Illustration disclosure: the image used in the company-logo position is an illustrative Pexels stock photograph of grocery-store shelves. It is not an official Amazon logo or Amazon imagery, does not represent a verified Amazon facility and does not imply Amazon's endorsement.
This role makes forecasting an operational decision problem rather than an isolated statistical exercise. Demand and labor forecasts influence staffing commitments, capacity allocation, cost and customer service across Amazon's grocery network. The relevant objective is not simply to improve model accuracy, but to determine whether better forecasts produce better labor and capacity decisions at an acceptable operating cost. Value depends on evaluating forecast errors according to their downstream consequences, including the different costs of understaffing and overstaffing, and identifying which teams bear those consequences. The main failure mode is optimizing aggregate forecasting metrics while making staffing or capacity decisions worse for specific locations, periods or business stakeholders. The posting demonstrates investment in applied forecasting science, not independently verified operational improvement.