Applied Scientist, Execution Planning Science & Engineering, Amazon (10562813)
Amazon.com Services LLC is recruiting an Applied Scientist in Bellevue, Washington, for Execution Planning Science & Engineering within NA AMZL Supply Chain (job ID 10562813). The role focuses on last-mile associate labor planning, supply chain network optimization and automated operational decision-making using statistics, machine learning and numerical optimization. Responsibilities include experimentation, impact measurement and production systems. The employer lists a PhD or a Master's degree with at least four years of relevant experience, at least three years building models for business applications, programming, numerical optimization and scientific publications or patents among the qualifications. The stated annual base salary is USD 142,800-193,200, excluding sign-on payments and restricted stock units (RSUs). The employer's Apply now link led to Amazon Jobs account authentication when checked on October 17, 2026. The post-login form was not tested and no application was submitted. October 17 is the availability verification date, not the original posting date, which is unknown. Work arrangement, contract type and working-time category have not been established and must not be inferred.
Generic industrial illustration only, not an Amazon facility. Photo: Galib Rahman Nadim / Pexels. Source: https://www.pexels.com/photo/factory-worker-supervising-conveyor-belt-operations-39351224/ - License: https://www.pexels.com/license/
Crédit photo (FR) : photographie industrielle illustrative, sans représentation d'une installation Amazon. Photo : Galib Rahman Nadim / Pexels. Source : https://www.pexels.com/photo/factory-worker-supervising-conveyor-belt-operations-39351224/ - Licence : https://www.pexels.com/license/
Last-mile labor planning reveals where supply chain AI must become operationally feasible optimization.
The challenge is not simply predicting parcel volumes or staffing demand. It is matching workforce availability and network capacity to speed, reliability and cost objectives under changing conditions. Amazon's advertised role combines statistical modeling, numerical optimization, controlled experimentation and production deployment. A useful decision system must improve measurable service and labor outcomes while satisfying actual staffing and network constraints.
The main failure mode is a mathematically efficient labor plan that cannot be executed reliably. This vacancy signals scientific hiring priorities, not proven deployment gains or a newly launched Amazon product.