As it strives to be Earth's most customer-centric company, Amazon has grown its Last Mile delivery network, accelerating customer delivery times and providing innovation to customers. Amazon’s Last Mile programs deliver to homes, businesses, Amazon Lockers, and even cars all over the world. This network is powered by thousands of small businesses and hundreds of thousands of drivers that leverage Amazon's technology to deliver millions of smiles to customers each day.
The Last Mile Economics Team draws from a wide range of technical skillsets in economics, statistics, machine learning, and operations research to develop innovative forecasting and optimization tools to help scale this exponentially growing logistics network. This role affords an opportunity to work on large, complex, and technically challenging problems, while directly contributing to driving an improved experience for Amazon customers and our delivery partners.
We are looking for candidates with strong skills in Optimization (Mixed Integer Programming, Dynamic Programming), as well as solid skills in Python coding and data collection and analysis. Some background in Machine Learning (Forecasting, Reinforcement 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, apply them to define strategic and tactical needs, and drive the appropriate business and technical solutions in the areas of delivery planning, supply chain optimization, pricing, incentives, and capacity planning.
• 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, MATLAB, Mosel 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.
