Data Scientist II, Last Mile Science

Does the thought of improving one of the world’s most complex logistic systems inspire you? Is your passion to sift through hundreds of systems, processes, and data sources to solve the puzzle and identify the next big opportunity? Are you a creative big thinker who is passionate about using data to direct decision making and solve complex and large-scale challenges? Are you fascinated by the interactions between operations and strategy? Do you feel like your skills uniquely qualify you to bridge communication between teams with competing priorities? If so, then this position is for you!

Come help Amazon create state-of-the-art science-driven technologies for delivering packages to the doorstep of our customers! The Last Mile Routing & Planning organization builds the software, algorithms and tools that make the “magic” of home delivery happen: our flow, sort, dispatch and routing intelligence systems are responsible for the billions of daily decisions needed to plan and execute safe, efficient and frustration-free routes for drivers around the world. Our team supports deliveries (and pickups!) for Amazon Logistics, Same Day, Amazon Grocery, Lockers, and other new initiatives across the world.

Key job responsibilities
In this role, your main focus will be to apply algorithms, synthesize information, identify business opportunities, provide data-driven insights and communicate business and technical requirements within the team and across stakeholder groups. You will partner closely with other scientists and engineers in a collegial environment with a clear path to business impact. We have an exciting portfolio of research areas including vehicle routing, planning for electric and autonomous vehicles, district and stops planning, ultra-fast deliveries, fleet planning, and forecasting solutions for different delivery programs leveraging the latest OR, ML, and Generative AI methods, at a global scale. Successful candidates will have a deep knowledge of Operations Research and/or Machine/Deep Learning methods, experience in applying these methods to large-scale business problems, the ability to map models into production-worthy code in Python or Java, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the excitement to take iterative approaches to tackle big research challenges.