Microsoft AI (MAI) builds an integrated consumer AI ecosystem across search, browsing, and content, focused on delivering trustworthy, scalable experiences with durable user and business value. The MAI Ecosystem Data Science Team owns MAI-wide metrics, shared measurement systems, and experimentation frameworks to enable consistent, high-confidence decisions and optimize MAI-level outcomes. We are seeking a Principal, Data Science & Analytics for ecosystem data science to own cross product measurement strategy, partner across product and business teams, and uphold a high bar for metric quality, statistical rigor, and data driven leadership.

Leadership: Mentor data scientists and align work with MAI ecosystem goals, driving technical excellence, innovation, and cross-team collaboration.

Data Strategy & Execution: Develop ecosystem data strategies for marketplace and system performance, including standardized data collection, analysis, reporting, and interpretation; validate analytical approaches and results.

Advanced Analytics & Measurement: Apply machine learning, statistical modeling, data mining, and experimentation to large datasets; define and deliver metrics that accurately measure user and business value across products and marketplace components.

Experimental Design & Implementation: Design and execute experiments across user and demand dimensions; translate strategy into clear, actionable, and measurable plans, sharing progress and results with stakeholders.

Collaboration: Partner closely with product, program management, engineering, and business teams to integrate data science solutions into shared platforms and marketplace operations.

Performance Optimization: Identify cross-team opportunities for product and process improvement; implement data-driven solutions to improve efficiency, reliability, and user experience.

Influence & Decision-Making: Engage stakeholders with clear, compelling, and actionable insights; make independent decisions for the team and handle complex tradeoffs to drive product and service improvements.

Technical & Operational Leadership: Develop and standardize processes for data acquisition, quality, and operationalizing ML models; provide expert review of analysis and modeling; lead adoption of new tools and technologies to improve availability, reliability, efficiency, and performance.

Standards & Trusted Advisory: Establish and uphold standards, policies, and best practices for high-quality, efficient, and extensible code; influence business, customer, and solution strategy with a strong customer focus; act as a trusted advisor across the ecosystem.