Description The Supply Chain Optimization Technologies (SCOT) organization owns Amazon’s global inventory management systems: we build systems that decide what, when, where, and how much we should buy to support Amazon’s business and to make our customers happy. We do this for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. Our systems are built entirely in-house, and are on the cutting edge in automated large-scale business, inventory and supply chain planning and optimization systems. We foster new game-changing ideas, creating ever more intelligent and self-learning systems to maximize the efficiency of Amazon's inventory investment and placement decisions. The Automated Inventory Management team (AIM) within SCOT seeks an experienced and motivated Sr.Applied Scientist to develop analytical models and tools to automate the auditing of the SCOT systems. Such tools may include algorithms, metric bridges, dashboards, processes and workflow systems. The successful candidate will have strong quantitative data mining and modeling skills and be comfortable working on new and highly ambiguous problems from concept through to execution. They will have strong communication and leadership skills, will be able to collaborate with other teams (e.g. software development, business owners, product managers) and to present findings to senior audiences to drive business improvements. Key job responsibilities Responsibilities Include: - Implement statistical and machine learning methods to solve complex business problems - Research new ways to improve predictive and explanatory models - Directly contribute to the design and development of automated prediction systems and ML infrastructure - Build models that can detect supply chain defects and explain variance to the optimal state - Collaborate with other researchers, software developers, and business leaders to define the scientific roadmap for this team Basic Qualifications - 3+ years of building machine learning models for business application experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning Preferred Qualifications - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. - Master's degree, or PhD