Senior Data & Applied Scientist

Microsoft Microsoft · Big Tech · United States · Data Science

Senior Data & Applied Scientist role focused on product/program analytics and insights for Microsoft's Global Skilling initiatives, including the AI Skills Navigator. The role involves understanding learner behavior, defining success metrics, designing experiments, and partnering with engineering and product teams to improve learning experiences and accelerate proficiency. It leverages data science techniques to measure product performance and learning outcomes.

What you'd actually do

  1. Own product/program analytics and insights for Microsoft skilling experiences to drive product/program strategy and improve the end-to-end learner experience.
  2. Design and analyze learner journeys and funnels to understand behavior across discovery, engagement, completion, and retention.
  3. Define and operationalize product/program success metrics (north star, input metrics, and guardrails), and content quality/relevance metrics. Conduct data quality checks.
  4. Build measurement frameworks and new metrics to quantify learner proficiency and skill growth (e.g., consumption signals, certifications, and expertise indicators).
  5. Conduct deep-dive analyses to identify drivers of engagement, proficiency, and retention; surface opportunities and risks.

Skills

Required

  • Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience OR equivalent experience.
  • Product/program analytics experience
  • Customer journey and funnel analytics
  • Causal measurement
  • Experimentation frameworks
  • Statistical methods
  • Data storytelling
  • Learner behavior analysis
  • Product telemetry
  • Data instrumentation
  • Scalable reporting
  • Competency models
  • Skill taxonomies
  • Certification readiness frameworks
  • User privacy
  • Consent
  • Retention expectations

Nice to have

  • 4+ years of experience in data science, product/journey analytics, causal inference, and user behavioral modeling.
  • Experience driving product improvements through data and insights.
  • Proficiency in Python, R, SQL, KQL, PySpark, and modern analytics frameworks.
  • Experience designing experiments, defining standardized metrics, performing causal analyses, and delivering behavior-driven insights.
  • Experience with learning platforms and/or learner competency and skill modeling (e.g., proficiency, mastery, and skill signals).
  • Experience levering AI to deliver accelerated learning outcomes.

What the JD emphasized

  • AI skilling
  • AI Skills Navigator
  • agentic learning experience
  • learner proficiency
  • skill growth
  • product/program analytics
  • customer journey and funnel analytics
  • causal measurement
  • experimentation frameworks
  • learner behavior
  • product telemetry
  • data foundations
  • product strategy
  • user experiences
  • skill gaps
  • learner proficiency
  • learning content
  • curriculum

Other signals

  • AI skilling
  • AI Skills Navigator
  • agentic learning experience
  • learner proficiency
  • skill growth