Note: The job is a remote job and is open to candidates in USA. Microsoft's Global Skilling team helps people and organizations build skills through accessible, personalized learning experiences. The Senior Data & Applied Scientist will lead product and program analytics for Microsoft skilling experiences, using statistical methods, experimentation, causal analysis, and data storytelling to improve learner outcomes. The role partners with Product Management, Engineering, Design, User Research, Sales, Marketing, and Finance to shape telemetry, define metrics, and inform product strategy.
Responsibilities
- Own product/program analytics and insights for Microsoft skilling experiences to drive product/program strategy and improve the end-to-end learner experience
- Design and analyze learner journeys and funnels to understand behavior across discovery, engagement, completion, and retention
- Define and operationalize product/program success metrics (north star, input metrics, and guardrails), and content quality/relevance metrics. Conduct data quality checks
- Build measurement frameworks and new metrics to quantify learner proficiency and skill growth (e.g., consumption signals, certifications, and expertise indicators)
- Conduct deep-dive analyses to identify drivers of engagement, proficiency, and retention; surface opportunities and risks
- Plan and evaluate experiments (A/B tests) and causal analyses to measure impact of new features and changes; communicate actionable recommendations
- Set up operating rhythms (weekly business review metrics, experiment readouts, KPI health dashboards) so insights consistently change product decisions
- Partner with Engineering and Product/Program Management to shape product telemetry, data instrumentation, and scalable reporting to enable self-serve insights
- Develop, operationalize, and evolve competency models, skill taxonomies, and certification readiness frameworks to measure proficiency progression and mastery
- Evaluate the effectiveness of learning content and curriculum through causal analysis, sequential behavior signals, and outcome-based performance
- Identify which content, modalities, and learning assets drive the highest retention, mastery, certification success, and deployment readiness
- Use behavioral signals and multi-modal modeling to recommend improvements to content strategy, learning design, and curriculum sequencing
- Mentor other data scientists, review analyses, set a high bar for analytical rigor
- Ensure telemetry and analyses meet user privacy, consent, retention expectations
Skills
- Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) 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 3+ 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 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience
- 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 accelerate time to insight and depth of insights
- Hands-on experience with large-scale enterprise data platforms (e.g., Fabric, Synapse, ADX, Delta Lake, ADF, Databricks, Snowflake)
Benefits
- Certain roles may be eligible for benefits and other compensation.
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