Microsoft’s Cloud business is expanding rapidly, and the Cloud Supply Chain (CSCP) organization is responsible for enabling the hardware infrastructure that powers this growth — including AI. CSCP’s vision is to empower customers to achieve more by delivering Cloud and AI capabilities at scale, and its mission is to deliver the world’s computer with an industry-leading supply chain. In addition to traditional supply chain functions such as plan, source, make, and deliver, CSCP also manages supportability (spares), sustainability, and global decommissioning of datacenter assets. 

We deliver the core infrastructure and foundational technologies for more than 200 Microsoft online businesses — including Bing, Office 365, Xbox Live, OneDrive, and the Azure platform — serving over 1 billion customers in 90+ countries through 300+ datacenters worldwide. 

The Microsoft Capacity Planning (MCP) team within CSCP is tasked with planning all aspects of capacity across the Azure fleet. Our aspiration is to provide fully automated and highly optimized capacity planning systems. As a member of MCP, you will collaborate with engineers, program managers, and planners across CSCP, translating business problems into scalable solutions and driving them end-to-end from design to production. You will also contribute to strategic decisions for all capacity-impacting features. 

We are seeking a talented Data Scientist to develop and apply advanced methods in operations research, stochastic optimization, simulation, and large-scale mathematical programming, augmented by core computer science disciplines such as algorithms, data structures, and distributed computing. Your work will focus on capacity planning, inventory optimization, and scenario analysis, designing research-driven models to solve complex challenges in cloud supply chain planning. 

The impact of your work will be measured through improvements in Azure capacity fulfillment, platform efficiency, COGS reduction, and customer satisfaction. Our team partners closely with engineering, applying state-of-the-art machine learning and constrained optimization techniques to some of the most complex real-world problems at scale. If you are excited by cutting-edge methods and their application in a dynamic, global environment, you will thrive on our team as we deliver world-class innovation.