Principal, Data Science & Analytics

Microsoft Microsoft · Big Tech · Redmond, WA +2 · Data Science

This role focuses on ecosystem data science within Microsoft AI, owning cross-product measurement strategy, shared systems, and experimentation frameworks. The principal will apply machine learning, statistical modeling, and experimentation to large datasets to define and deliver metrics that measure user and business value across products. Responsibilities include mentoring, developing data strategies, designing and executing experiments, collaborating with product and engineering teams, and establishing standards for data quality and operationalizing ML models. The role aims to optimize MAI-level outcomes and drive data-driven decision-making.

What you'd actually do

  1. Develop ecosystem data strategies for marketplace and system performance, including standardized data collection, analysis, reporting, and interpretation; validate analytical approaches and results.
  2. 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.
  3. Design and execute experiments across user and demand dimensions; translate strategy into clear, actionable, and measurable plans, sharing progress and results with stakeholders.
  4. Partner closely with product, program management, engineering, and business teams to integrate data science solutions into shared platforms and marketplace operations.
  5. 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.

Skills

Required

  • Doctorate or Master's or Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
  • 5+ years (or 7+ or 10+ depending on degree) of data-science experience
  • managing structured and unstructured data
  • applying statistical techniques
  • reporting results

Nice to have

  • Doctorate or Master's or Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
  • 8+ years (or 10+ or 12+ depending on degree) of data-science experience
  • managing structured and unstructured data
  • applying statistical techniques
  • reporting results

What the JD emphasized

  • high bar for metric quality
  • statistical rigor
  • data driven leadership
  • creative modeling geeks
  • challenge the status quo
  • data driven solutions to ambiguous problems
  • operationalizing ML models
  • high-quality, efficient, and extensible code

Other signals

  • ecosystem data science
  • cross product measurement strategy
  • experimentation frameworks
  • optimize MAI-level outcomes
  • machine learning
  • statistical modeling
  • data mining
  • experimentation
  • define and deliver metrics
  • operationalizing ML models