Data Scientist, Middle Mile Transportation Technology Amazon
Seattle, WA

Job Description

Amazon seeks motivated and innovative individuals with strong analytical skills and practical experience to join our Middle Mile Air and Ground Optimization team. We are hiring specialists at all levels for our scientific team with expertise in machine learning and demand forecasting, transportation and systems engineering, network and combinatorial optimization, stochastic optimization and algorithm design.

Middle Mile Air and Ground transportation represents one of the fastest growing logistics areas within Amazon. Amazon Fulfillment Services transports millions of packages via air and ground and continues to grow year over year. The scale of this operation challenges Amazon to design, build and operate robust transportation networks that minimize the overall operational cost while meeting all customer deadlines. The Middle Mile Modeling and Optimization group is charged with developing an evolving suite of decision support and optimization tools to facilitate the design of efficient air and ground transport networks, optimize the flow of packages within the network to efficiently align network capacity and shipment demand, and effectively utilize scarce resources, such as aircraft and trucks. Time horizons for these tools vary from years and months for long-term planning to hours and minutes for near-term operational decision making and disruption recovery. These tools rely heavily on mathematical optimization, stochastic simulation, meta-heuristic and machine learning techniques. In addition, Amazon often finds existing techniques do not effectively match our unique business needs which necessitates the innovation and development of new approaches and algorithms to find an adequate solution.

This is an L4, L5 or L6 Research Scientist and Data Scientist role depending on professional experience.

Amazon is an Equal Opportunity-Affirmative Action Employer - Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation