About the job
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.
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
Minimum
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
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 8+ 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.
6+ years of experience in Python, R, C++, Java, C#, or similar programming languages.
Demonstrated expertise in statistics, experimentation, causal inference, and large-scale data analysis.
Experience developing metrics, evaluation frameworks, and measurement systems that drive product and business decisions.
Proven track record of identifying high-impact opportunities and solving complex, ambiguous problems across multiple organizations.
Experience influencing product strategy and driving alignment among stakeholders with differing objectives through data-driven insights and recommendations.
Exceptional written and verbal communication skills, with the ability to translate complex technical concepts into clear guidance for executive and non-technical audiences.
Experience leading large cross-functional initiatives and collaborating effectively across product, engineering, business, and analytics teams without direct authority.
Experience building and evaluating production-scale data, analytics, machine learning, or experimentation systems.