Amazon - Bellevue, WA
posted 3 months ago
The Business Intelligence Manager for WW FBA Inventory Defects and Reimbursements at Amazon.com Services LLC is a pivotal role that involves leading a team of business intelligence engineers to deliver insights and analysis across Amazon's product portfolio. This position is situated within the Defect Prevention and Reconciliation (DP&R) organization, which is responsible for building innovative technical products that recognize, reconcile, and prevent exceptions within Amazon's end-to-end supply chain. The successful candidate will be tasked with owning metrics, dashboarding, and measurement across various platforms and products, directly influencing Amazon's business and customer promise. In this role, you will manage and lead a team of Business Intelligence Engineers, overseeing organization-wide analytical solutions, including metrics and attributions. You will collaborate with in-house scientists, product management, engineering, and business teams to identify new business intelligence capabilities and projects. Your responsibilities will include contributing to the design, implementation, and delivery of BI solutions for complex and ambiguous problems, as well as analyzing and solving problems at their root to understand the broader context. The position requires a strong analytical mindset and outstanding business acumen. You will utilize data mining, model building, and other analytical techniques to develop and maintain customer segmentation and predictive models that drive business decisions. Additionally, you will identify opportunities and key criteria to enhance analytical reporting and business strategy, producing written recommendations and insights for key stakeholders to shape effective metric development and reporting. Simplifying and automating reporting, audits, and other data-driven activities will also be a key focus, along with developing and driving best practices in data integrity, consistency, analysis, validations, and documentation. The role demands a self-starter who is comfortable with ambiguity, has a strong attention to detail, and enjoys working with large-scale data.