Description

　　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 as well as predict the impact of new supply chain initiatives.

　　We are looking for applied scientists 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 Research Scientist, 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 perform research across various system using multiple disciplines. Much of the job will require close collaboration with software development engineers, other scientists, and business teams to innovate for and solve problems of the future.

　　Key job responsibilities

　　Candidates will be responsible for developing solutions to better manage and develop supply chain simulation system. The successful candidate should have solid research experience in one or more technical areas of Operations Research or Machine Learning. These positions will focus on identifying and analyzing opportunities to improve existing algorithms and also on developing the next generation of Amazon's inventory optimization systems. They require superior logical thinkers who are able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and work closely with the engineering team to implement the solution. To support their proposals, candidates should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. 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.

　　We are open to hiring candidates to work out of one of the following locations:

　　Bellevue, WA, USA | New York, NY, USA

　　Basic Qualifications

　　PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

　　1+ years of building models for business application experience

　　Experience in patents or publications at top-tier peer-reviewed conferences or journals

　　Experience programming in Java, C++, Python or related language

　　Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

　　Preferred Qualifications

　　Experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF

　　Experience in professional software development

　　Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

　　Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. Applicants should apply via our internal or external career site.