Data Science Strategy Lead

Microsoft•Redmond, WA

19h

About The Position

Microsoft’s Cloud business is expanding, and the Cloud Supply Chain (CSCP) organization is responsible for enabling the hardware infrastructure underlying this growth including AI! CSCP’s vision is to empower customers to achieve more by delivering Cloud and AI capabilities at scale. Our mission is to deliver the world's computer with an industry-leading supply chain. The CSCP organization is responsible for traditional supply chain functions such as plan, source, make, deliver, but also manages supportability (spares), sustainability, and decommissioning of datacenter assets worldwide. We deliver the core infrastructure and foundational technologies for Microsoft's over 200 online businesses including Bing, MSN, Office 365, Xbox Live, OneDrive and the Microsoft Azure platform for external customers. Our infrastructure is supported by more than 300 datacenters around the world that enable services for more than 1 billion customers in over 90 countries. Microsoft Cloud Planning (MCP) is the central planning function within CSCP focused on forecasting, demand planning, and supply planning for all Microsoft Cloud services and associated hardware, directly impacting the success of Microsoft's cloud business. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. #CSCP #CSCPJobs

Requirements

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR equivalent experience.
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
These requirements include, but are not limited to, the following specialized security screenings:
Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR equivalent experience.
Advanced experience applying statistical modeling or machine learning techniques to complex business problems, with demonstrated ability to translate analysis into actionable, decision driving outcomes.
Experience defining product or capability vision, building roadmaps, and making long term investment trade offs informed by data, customer needs, and business priorities.
Proven ability to influence Data Science direction beyond individual projects and shaping strategy, standards, or investment decisions across teams or domains.
Demonstrated strength in framing ambiguous problem spaces, establishing success metrics, and balancing analytical rigor, scalability, and business practicality.
Experience leading complex, cross functional initiatives involving Data Science, engineering, product, and business partners, with accountability for delivery and adoption.
Ability to collaborate effectively across organizational boundaries and operate as a trusted advisor in highly ambiguous environments.
Strong executive communication and storytelling skills, with the ability to influence senior stakeholders through clear, data driven narratives rather than formal authority.

Responsibilities

Formulate Data Science Strategy: Set the long term Data Science strategy, defining where to intentionally invest, differentiate, and deprioritize to maximize sustained business value.
Develop Multi Year Vision and Roadmap: Create a multi year vision and roadmap for Data Science capabilities, grounded in the voice of the customer, incorporating external perspectives such as industry trends, emerging technologies, best practices, and the voice of customer.
Shape Portfolio Level Decisions: Establish and apply frameworks to evaluate Data Science initiatives, making explicit stop, continue, pause, or pivot recommendations based on strategic priorities.
Strategic Business Integration and Value Framing: Define and strengthen the engagement and decision interface between Data Science and business partners, ensuring clarity in problem framing, success metrics, and prioritization ownership.
Articulate Business Value of Data Science: Translate analytics and AI capabilities into measurable business outcomes such as decision quality, agility, resiliency, productivity, speed, and scale, and ensure these outcomes guide prioritization and investment decisions.
Technical Advisory and Strategic Enablement: Provide technical and strategic guidance on how business intent should be translated into modeling choices, platforms, architectures, and dependencies aligned with long term Data Science strategy.
Evolve Data Science Operating Model: Influence operating models, role clarity, and collaboration patterns across Data Science, engineering, product, and business teams to improve effectiveness, accountability, and integration of insights into business outcomes.
Drive Cross Organizational Execution: Orchestrate execution of complex, cross organizational initiatives by aligning stakeholders, managing dependencies, and ensuring Data Science solutions are operationalized and adopted at scale.
Executive Communication and Influence: Communicate strategy, trade offs, progress, and impact clearly to senior and executive stakeholders through concise, data driven narratives that inform decisions.

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume