Are you looking for a challenge? Imagine being part of a team that owns one of the largest supply chain simulation systems in the world to predict inventory flows for the millions of items available on Amazon.com worldwide. Inventory planning involves many algorithms to buy inventory in the right quantities, at the right frequencies, from the right vendors, and assigning to the best warehouse to fulfill customer demand to optimize long term free cash flow for Amazon. Our system lives at the heart of these algorithms, keeping up with the rapid pace of optimization improvements and simulating how they interact with each other. We simulate what these systems will do for months into the future, predicting inventory flows and key operational and financial metrics across the network. This experimentation platform is critical in understanding labor needs, managing our network capacity, and allowing continued optimizations to the many algorithms we simulate. Imagine enabling Amazon's supply chain systems to make data driven decisions based on simulations of trillions of inventory events per day.

Every time an Amazon customer makes a purchase, a number of systems are involved: these systems help optimize inventory acquisition, enable a number of purchase options, ensure great pricing, store products so they are available for fast delivery, and minimize package frustration. The Supply Chain Optimization Technology (SCOT) Group develops and manages these systems. We are central to Amazon customers' ability to find what they want and get it when they want it. Within SCOT, the Inventory Planning Control (IPC) Simulation team is responsible for designing and executing the simulations to predict inventory flows for labor planning, predict the impact of new supply chain initiatives, and enable experimentation of new inventory policies developed by SCOT teams, expediting their development cycle.

We are looking for a science manager to drive research innovation in SCOT by pushing IPC Simulation systems further upstream in the innovation process, developing new techniques and methodologies for both experimentation and prediction use cases, and applying existing models to our problem space and beyond. As an Amazon Science Manager, your work will impact on how we serve our customers so that they get the right product at the right time. In our team, you will be working in one of the world's largest data warehouse environments. You need to be a sophisticated user of data querying tools and advanced quantitative and modeling techniques, and an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication to drive change, as your work will be visible up to the highest business leaders in SCOT. You will own the roadmap and vision of IPC Simulation. Much of the job will require close collaboration with software development engineers, other scientists, science managers, and business teams to innovate for and solve problems of the future.


Key job responsibilities
As a Senior Applied Science Manager in IPC, you will partner with the senior tech leaders in the organization to define the long term architecture of our decision optimization and prediction systems. You will play a key role in developing long term strategic solutions that have business impact beyond the scope of the organization. You will bring technical expertise in several technical areas of Operations Research or Machine Learning, and are able to help team to define and deliver the science vision. You will ensure senior leaders in the organization are up to speed on important trends, tools and technologies and how they will be used to impact the business. You are able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs.