The AWS Infrastructure Planning group is responsible for planning and coordinating a complex, multi-tier supply chain that delivers capacity for all AWS services. This includes data center setup, equipment purchase, installation and operation of servers with power and cooling, inventory management and other such decisions. We're building a new suite of tools to automate all AWS supply chain planning, with a broad charter that involves inventory optimization, placement, vendor allocation, transition management, lead time predictions, and more. We are responsible for ensuring that the AWS cloud remains elastic for its customers by taking care of all of the back-end complexity, enabling our infrastructure to stay ahead of our rapid growth.As an Applied Scientist you will use your experience to develop new strategies to improve the performance of AWS Infrastructure’s planning systems and networks. Working closely with fellow applied scientists and product managers, you will use your experience in modeling, optimization, and simulation to design novel algorithms and models of new policies, simulate their performance, and evaluate their benefits and impacts to cost, reliability, and speed of our supply chain.We are looking for experience in network and combinatorial optimization, algorithms, data structures, statistics, and/or machine learning. You will have an opportunity to work on large mathematical problems, with large elements of unpredictability. You will write and solve linear and mixed-integer problems to find optimal solutions to build decisions given capacity constraints and the demand distributions. You will also drive process changes that comes with automation and smarter optimization.Key Responsibilities:· Design and develop mathematical, simulation and optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of inventory management, network flow, supply chain optimization, demand planning.· Apply theories of mathematical optimization, including linear programming, combinatorial optimization, integer programming, dynamic programming, network flows and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software.· Prototype these models by using modeling languages such as R, MATLAB, Mosel or in software languages such as Python.· Create, enhance, and maintain technical documentation, and present to other Scientists, Product, and Engineering teams.· 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.