As part of Amazon Last Mile Science & Technology organization, you’ll partner closely with other scientists and engineers in a collegial environment with a clear path to business impact. We have an exciting problem area to innovate in topology, routing planning, routing inputs, capacity planning and dispatch solutions for different last mile programs leveraging the latest OR, computer vision, ML, and deep learning techniques. The team is actively looking to hire research scientist or applied scientist to lead the area. Successful candidates will have a deep knowledge of Operations Research and Machine Learning methods for large scale problems, the ability to map models into production-worthy code, 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, long-term problems.
We are looking for candidates with strong skills in Optimization modeling (Mixed Integer Programming, Dynamic Programming, Decomposition Methods), as well as solid skills in Python coding and data collection and analysis. Some background in Control Theory, Machine Learning, and Economics would be helpful too.
The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail, an ability to work in a fast-paced and ever-changing environment and a desire to help shape the overall business.


Key job responsibilities
• Design and develop advanced mathematical, optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of routing planning, supply chain optimization, network optimization, economics, and control theory.
• Apply mathematical optimization and control techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming, decomposition methods, model predictive control) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software.
• Research, prototype, simulate, and experiment with these models by using modeling languages such as Python or R; participate in the production level deployment.
• Create, enhance, and maintain technical documentation
• Present to other Scientists, Product, and Software Engineering teams, as well as Stakeholders.
• Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans.
• Influence organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor other Scientists.