Senior Applied Scientist - Machine Learning Amazon
Seattle, WA

Job Description

At Amazon, our vision is to be Earth's most customer-centric company; to build a place where people can come to find and discover anything they might want to buy online. Amazon's Supply Chain Optimization Technologies (SCOT) team helps achieve this vision by building large scale inventory management and optimization systems that help manage the flow of tens of millions of products through world's most sophisticated supply-chain network in the most cost-effective way.

SCOT has a new team called FBA Inventory Optimization (FIO), and we are hiring! FIO is a cross-functional team of data, applied and research scientists, economists, engineers, and product managers focused on developing solutions that help drive growth, and reduce hundreds of millions of dollars in costs, for the fast-growing Fulfillment-By-Amazon (FBA) business. At FIO, we utilize cutting edge science, machine-learning, and distributed software on the Cloud to build systems that automate and optimize inventory management under the uncertainty of demand, pricing and supply.

FIO is seeking a Senior Applied Scientist to join the group! We are recruiting a curious and creative Applied Scientist who will collaborate with other scientists and engineers to leverage new machine learning methods and algorithms for the modeling and analysis of data. For FIO, machine-learning is a keystone technology that informs large scale systems which are aimed at (1) driving FBA growth and efficiency through new policies and incentives, (2) balancing the supply and demand of FBA capacity through market/contract design, (3) enabling profitable global selling opportunities, and (4) driving out costs across the FBA supply chain to spin the flywheel. If you are looking to innovate at scale, have an impact, work on a ground floor opportunity while bootstrapping a fast growing business, then, this is an opportunity that you don't want to miss.

An ideal candidate will be an expert in the areas of data science, machine learning and statistics who will have expertise in applying theoretical models in an applied environment. The candidate will be expected to work on numerous aspects of Machine Learning such as predictive modeling, probabilistic modeling, scalable inference methods and latent variable models including transfer learning, Gaussian processes, hyper-parameter tuning, and uncertainty quantification. Challenges will involve dealing with very large data sets and requirements on throughput.

You will routinely participate in the design, development, evaluation, and deployment of data-driven machine-learned (ML) models targeting diverse applications for FBA inventory optimization. You would be expected to make decisions about technology, models and methodology choices. You will strive for simplicity, and demonstrate judgment backed by statistical proof. You will also collaborate with the broader decision and research science community in SCOT and Amazon to broaden the horizon of your work and mentor engineers and scientists.