Principal Data Scientist Manager

Microsoft

11h

About The Position

At Worldwide Learning (WWL), our mission is to inspire every person and organization on the planet to reach their potential through learning. With technology evolving rapidly, having a skilled workforce is more important than ever. We believe that making learning accessible to all is key to fostering a diverse and inclusive culture, helping people excel both personally and professionally, and igniting innovation. We are seeking a Principal Data Scientist Manager with deep expertise in product and customer journey analytics, graph intelligence, causal measurement, and experimentation frameworks. In this role, you will architect the next-generation learning intelligence platform — including the learner graph, content effectiveness analytics, global skilling measurement systems, and adaptive skilling recommendations. You will partner across Microsoft Sales, Marketing, Finance, Engineering, and Global Skilling to deliver insights that shape investment strategy, improve readiness, close skill gaps, and accelerate cloud success. This is a role for a technical and strategic leader who can connect data science to business outcomes at global scale. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Requirements

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 7+ 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 10+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
3+ years people-management experience.

Nice To Haves

Doctorate 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 Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ 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.
5+ years people-management experience.

Responsibilities

Learning Insights, Skill Taxonomies & Competency Modeling
Lead product data insights for Microsoft internal and external skilling/learning solutions such as AI Skills Navigator to drive product improvement and better learning outcomes for users and customers.
Build a global learner graph representing relationships among skills, certifications, content, modalities, and multi-step learning pathways using graph intelligence techniques (GraphRAG, embeddings, entity resolution, GNNs).
Develop, operationalize, and evolve competency models, skill taxonomies, and certification readiness frameworks to measure proficiency progression and mastery.
Create multi-step journey analytics for learners, partners, and field roles using sequential modeling, trajectory prediction, and pathway optimization.
Apply LLMs and multi-modal embeddings to learning content (text, transcripts, assessments) to align content to skills, outcomes, and recommended learning paths.
Measurement, Experiments & Causal Impact
Invent and deliver standardized metrics and KPI frameworks that quantify learning outcomes, content effectiveness, competency development, and readiness improvement.
Lead A/B testing, uplift modeling, counterfactual analysis, and causal inference to evaluate the impact of learning programs, content interventions, and skilling experiences.
Build model evaluation frameworks including reliability scoring, bias detection, longitudinal cohort analysis, and scenario simulation.
Quantify the relationship between learning outcomes and downstream business impact such as customer success, partner readiness, deployment velocity, Azure consumption, and revenue performance.
Partner, Field & Customer Skilling Analytics
Partner with the Microsoft Partner Division, Global Partner Solutions (GPS), and Worldwide Learning to assess partner skilling, certification progress, capability gaps, and ecosystem readiness.
Work with field organizations to measure seller and engineer skilling, understand readiness gaps, and optimize role-based learning journeys.
Deliver insights that guide regional skilling investments, partner ecosystem development, customer enablement, and go-to-market programs.
Content Effectiveness & Learning Experience Analytics
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.
Modeling Excellence & Technical Leadership
Build scalable predictive and adaptive learning models using LLMs, GraphRAG, reinforcement learning, sequence models, and agentic AI.
Mentor data scientists on measurement science, model evaluation, experimental design, and enterprise-scale analytics best practices.
Cross-Org Collaboration & Strategic Influence
Partner across MCAPS (Sales, Support, Partners, Marketing, Finance), Product Engineering, and Worldwide Learning to align learning analytics with organizational strategy.
Influence senior leaders through clear, compelling, actionable insights that shape learning investments, partner incentives, field readiness programs, and customer enablement initiatives.
Champion a culture of experimentation, measurement, inclusive learning, and data-driven decision-making.
Embody our Culture and Values.