Principal Data Scientist
Microsoft
- Location
- United States, California, Mountain View; United States, Washington, Redmond
- Work model
- On-Site
- Level
- Principal
- H-1B history
- 2,066 approvals (FY2023)
- Posted
- 6h ago
About this role
Overview
Microsoft Copilot is building an ecosystem of AI-powered consumer experiences across Search, Copilot, Edge, MSN, and beyond. The MAI Ecosystem Data Science team defines the metrics, experimentation frameworks, and measurement systems that shape how Microsoft AI evaluates success, identifies opportunities, and makes investment decisions at scale. We are seeking a Principal Data Scientist to lead ecosystem-level measurement strategy across products and businesses. In this role, you will develop the scientific foundations that guide some of Microsoft's most important AI investments, partnering closely with product, engineering, business, and executive leaders to influence strategy, execution, and resource allocation. The ideal candidate combines deep expertise in experimentation, statistics, metrics, and causal inference with exceptional business judgment and influence. You thrive in ambiguity, challenge assumptions with data, and transform complex signals into clear decisions that drive product and business impact. This is a unique opportunity to shape how Microsoft AI measures value across the ecosystem, uncover new growth opportunities, and help define the future of AI-powered consumer experiences. 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. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
Responsibilities
Define the measurement strategy, metrics, and decision frameworks that guide product and investment decisions across the Microsoft AI ecosystem. Lead ecosystem-level analyses, experimentation, and causal inference to uncover opportunities, quantify impact, and drive business outcomes. Partner across product, engineering, business, and executive leadership teams to shape strategy, roadmap priorities, and resource allocation. Identify emerging opportunities, risks, and market dynamics before they become visible in product-level metrics. Design and evolve North Star metrics and evaluation systems that accurately measure user value, business impact, and long-term ecosystem health. Drive alignment and execution across organizations through influence, scientific rigor, and trusted partnerships. Raise the bar for analytical excellence through technical leadership, mentorship, and best practices in measurement, experimentation, and data science.
Qualifications
Required Qualifications: Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ 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 5+ 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 7+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
Preferred Qualifications
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field