How can we optimize headcount planning process to deliver the best experience for our sellers, vendors, brand and associates? How to leverage Machine Learning (ML) models including Random Forests, Support Vector Machines, Recursive Neural Networks to predict key planning inputs such as Average Handle Time, Incoming Seller /Vendor contacts, Associate Attrition etc.? How to optimize day-to-day staffing by leveraging intra-day data on multiple key planning inputs? How to evaluate the causal impact of various targeted programs that aim to improve associate productivity and job satisfaction, seller retention etc.?
The answers to these questions and others like them are core to helping Amazon's Marketplace business in many ways, including delivering best-in-the-class seller, vendor and associate experience. Our cross-functional team works closely with various stakeholders world-wide such as Finance, Operations, Regional Capacity Planners, Global Planning, Hiring and Training to help them make data-driven business decisions.
Using Amazon's large-scale computing resources including AWS EC2 clusters the Applied Scientist will build ML models to take a deep dive on the headcount planning process and work with domain experts and engineers to help turn those models into production solutions. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit sellers, vendors, brands and associates on the Amazon Marketplace platform. We are looking for a passionate, hard-working, and talented Research / Applied scientist who has experience building mission critical, high volume applications that customers love. You will have an opportunity to make a large impact on the design, architecture, and implementation of cutting-edge products used every day, by people you know.
Amazon is a company operating a marketplace for consumers, sellers, and content creators.