Bellevue, WA, US
7 days ago
Manager Business Intelligence, Supply Chain Innovation
Amazon's Global Supply Chain (GSC) is looking for a Business Intelligence Manager for the Supply Chain Innovation (SCI) team. SCI plays an important role in defining and shaping Amazon's inbound supply chain's long-term strategy. The team is responsible for the incubation and execution of highly visible supply chain projects across low-cost storage, bulk fulfillment, inbound cross-docks, fast-deploy fulfillment, and network optimization. Their solutions address the needs of the inbound network in order to meet the speed, cost, and resiliency demands of Amazon's customers.

In this role, you will support the Amazon Warehousing and Distribution Business Intelligence & Enablement (BIE) team as their manager. You will be responsible for leading a team of business intelligence analysts and driving data-driven decision making across the inbound supply chain.

Key job responsibilities
• Partner closely with business owners to understand their challenges, needs, and opportunities across the AWD network. Help the BIEs deep dive into operations execution and business problems, using analytical and statistical rigor to identify trends and opportunities, and present written and verbal analyses to drive improvements.
• Translate business requirements into data-driven insights to improve business results through creating best-in-class metrics, analysis, visualizations, and data-driven storytelling
• Manage a team of BIEs to deliver high-quality, actionable reporting and dashboards and adopt best practices in reporting and analysis, including understanding data needs, data integrity, test design, analysis, validation, and documentation.
• Drive the implementation of process and tool improvements based on data-driven recommendations
• Communicate findings and recommendations effectively to cross-functional stakeholders
• Identify opportunities to enhance reporting capabilities and drive continuous improvement
Adopting best practices in reporting and analysis: data needs, data integrity, test design, analysis, validation, and documentation
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