Data Scientist, Supply Chain Data Science

1+ years of data querying languages (e.G. SQL), scripting languages (e.G. Python) or statistical/mathematical software (e.G. R, SAS, Matlab, etc.) experience- 2+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources experienceAre you a data enthusiast? Does the world's most complex logistic systems inspire your curiosity? Is your passion to navigate through hundreds of systems, processes, and data sources to solve the puzzles and identify the next big opportunity? Are you a creative big thinker who is passionate about using data and optimization tools to direct decision making and solve complex and large-scale challenges? Do you feel like your skills uniquely qualify you to bridge communication between teams with competing priorities? If so, then this position is for you! We are looking for a motivated individual with strong analytic and communication skills to join the effort in evolving the network we have today into the network we need tomorrow.Amazon's extensive logistics system is comprised of thousands of fixed infrastructure nodes, with millions of possible connections between them. Billions of packages flow through this network on a yearly basis, making the impact of optimal improvements unparalleled. This magnificent challenge is a terrific opportunity to analyze Amazon's data and generate actionable recommendations using optimization and simulation. Come build with us!Key job responsibilitiesStatistical Models (ML, regression, forecasting, ) Optimization models, AB and hypothesis testing, Bayesian models. Communication skills with both tech and non tech stakeholders. Writting skills, capable to create documents for different types of readers (business, science, tech) to communicate results on analysis, testing.A day in the life- Knowledge of statistical packages and business intelligence tools such as SPSS, SAS, S-PLUS, or R- Experience with clustered data processing (e.G., Hadoop, Spark, Map-reduce, and Hive)