Business Intel Eng , Eu Sc Placement

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- Experience in analyzing and interpreting data with Redshift, Oracle, NoSQL etc. - Experience with data visualization using Tableau, Quicksight, or similar tools - Experience with one or more industry analytics visualization tools (e.g. Excel, Tableau, QuickSight, MicroStrategy, PowerBI) and statistical methods (e.g. t-test, Chi-squared) - Experience with scripting language (e.g., Python, Java, or R) - Experience with SQL - Experience in the data/BI space
Have you ever wondered how Amazon delivers so quickly, or if delivery speeds could improve even further? In the Amazon EU Supply Chain Placement Analytics & Engineering team, we drive innovation for our customers to enhance product availability and speed while reducing costs and carbon emissions. This role will give an unique view into Amazon Inventory Placement systems, processes, and operations, enabling collaboration with planning, operations, and technical teams. Among others, you will work to optimize connectivity in our inbound network. If you excel at understanding complex systems and have a proven track record of analyzing data to generate insights and business recommendations at scale, we'd like to talk to you. This position is based out of our EU Headquarters in Luxembourg. Key job responsibilities • Build and maintain metrics to evaluate and enhance Inventory Placement’s systems. • Create analytical tools and processes to accelerate insights and automation. • Drive placement strategy improvements using data modeling and analysis to optimize product spread across Amazon facilities, enhancing selection and reducing customer delivery distance. • Collaborate with technology teams to define key priorities to accommodate growing business needs and implement recommendations through new features and/or configurations. • Research, develop, document and present new opportunities to all levels of Supply Chain, Finance, Fulfilled By Amazon (FBA) and Retail leadership. A day in the life • Collaborate with a diverse team of Business Intelligence Engineers, Data Scientists, Program or Product Managers and Finance Analysts to develop effective metrics and analytical tools that address business goals. • Partner with Business and Tech teams to prioritize impactful insights. • Explore systems while performing anecdotes to understand concrete root causes. • Scale findings from anecdotes to understand its impact and respective prioritization. • Conduct data driven experiments to accelerate innovation. • Perform analysis at scale with new metric cuts and/or statistical models.