Sr Applied Scientist, Fulfillment Tech - Science

Overview

Sr Applied Scientist, Amazon Fulfillment Tech - Science Job :  |  Services LLC

The Amazon Fulfillment Technologies (AFT) Science team is seeking an exceptional Sr. Applied Scientist with strong operations research and optimization expertise to develop production solutions for one of the most complex systems in the world:
Amazon's Fulfillment Network. At AFT Science, we design, build, and deploy optimization, statistics, machine learning, and GenAI/LLM solutions that power production systems running across Amazon Fulfillment Centers worldwide. We tackle a wide range of challenges throughout the network, including labor planning and staffing, pick scheduling, stow guidance, and capacity risk management. Our mission is to develop innovative, scalable, and reliable science-driven production solutions that exceed the published state of the art, enabling systems to run optimally and continuously (from every few minutes to every few hours) across our large-scale network.

• Collaborate with scientists, software engineers, product managers, and operations leaders to drive the end-to-end lifecycle of optimization-driven solutions that directly impact process efficiency and associate experience in the worldwide fulfillment network.
• Develop deep understanding of operational processes, system architecture, and business requirements.
• Dive into data and code to identify opportunities for continuous improvement and disruptive new approaches.
• Design and develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and emerging challenges.
• Create prototypes and simulations for agile experimentation of proposed solutions.
• Advocate for technical solutions with business stakeholders, engineering teams, and senior leadership.
• Partner with software engineers to integrate prototypes into production systems.
• Design and execute experiments to test new or incremental solutions launched in production.
• Build and monitor metrics to track solution performance and business impact.

Amazon Fulfillment Technologies (AFT) designs, develops, and operates end-to-end fulfillment technology solutions that power Amazon Fulfillment Centers worldwide. AFT integrates software, science, and operational processes to optimize how inventory is received, stored, picked, packed, and shipped, enabling Amazon customers to receive the right products at the right time. AFT Science is the central science organization driving scientific solutions across inbound, outbound, and labor planning.

The Fulfillment Operations Research (FOR) team specializes in optimization, statistics, machine learning, and GenAI/LLM, partnering closely with software engineering, product, and operations teams to deliver scalable and reliable production solutions while advancing the state of the art in optimization, machine learning, and decision science.

• PhD, or Master’s degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• 3+ years of building machine learning models for business application experience
• 3+ years of experience building and deploying optimization models for business applications at scale
• Excellent written and verbal communication skills, with the ability to convey complex technical concepts to diverse audiences

• PhD with 6+ years of industry applied research experience and expertise in Operations Research, Optimization, ML/AI, Statistics, or an equivalent field
• Demonstrated success translating research into production systems and deploying production-grade code
• Proven track record of leading, mentoring, and growing scientists
• Ability to thrive in a fast-paced team environment, delivering robust technical solutions with complex dependencies and cross-functional requirements
• Experience in fulfillment, supply chain, logistics systems

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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