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Business Intelligence Engineer, IN SCOT, Automated Inventory Management
4 weeks ago
Key job responsibilities
• Work with Product Managers to understand customer behaviors, spot system defects, and benchmark our ability to serve our customers, improving a wide range of internal products that impact selection decisions both nationally and regionally.
• Design and develop end-to-end analytics solutions to monitor and optimize supply chain metrics, including Stock on Record Out of Stock (SoROOS) and Lost in Stock (LIS) at various business hierarchies.
• Create interactive dashboards and automated reporting systems to enable deep-dive analysis of inventory performance across multiple dimensions (ASIN/GL/Sub-category/LOB/Brand level).
• Build predictive models for seasonal demand forecasting and inventory planning, supporting critical business events and promotions.
• Create scalable solutions for tracking deal inventory readiness for small events and channel share management.
• Partner with category & business stakeholders to identify opportunities for process automation and innovation.
A day in the life
• Pioneering new analytical approaches and establishing best practices.
• Building solutions from the ground up with significant autonomy.
• Driving innovation in supply chain analytics through automation and advanced analytics.
• Making a direct impact on business performance through data-driven decision making.
About the team
Have you ever ordered a product on Amazon and when that box with the smile arrived, wondered how it got to you so fast? Wondered where it came from and how much it cost Amazon? If so, Amazon's Supply Chain Optimization Technology (SCOT) organization is for you. At SCOT, we solve deep technical problems and build innovative solutions in a fast-paced environment working with smart & passionate team members. (Learn more about SCOT: http://bit.ly/amazon-scot)- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
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