Principal Data Scientist

Microsoft•,

7d•$142,800 - $304,200

About The Position

At Global Skilling, our mission is to empower every person and organization to realize their potential through learning. As technology evolves at unprecedented speed, workforce readiness has become essential to innovation and growth. We are committed to making high-quality learning accessible, inclusive, and effective so that individuals and organizations can build critical skills, accelerate proficiency, and thrive in an AI-driven world. We are seeking a Principal Data Scientist to set the technical vision for measurement and learning intelligence across Microsoft skilling experiences, including AI Skills Navigator. This role will drive innovation in production-scale agentic solutions that continuously measure learner progress, personalize content and learning pathways, and transform complex behavioral and contextual signals into trusted, actionable insights. You will lead ambiguous, high-impact problem spaces from scientific formulation through production deployment, shaping product strategy, platform architecture, and investment decisions at global scale. This candidate is a recognized technical and strategic leader with product and program analytics, customer journey measurement, causal inference, experimentation, behavioral modeling, and applied AI. You will establish scientific standards and a multi-year roadmap for the skilling data and insights platform; architect scalable agentic capabilities; and raise the bar for model quality, reliability, responsible AI, and production engineering. Through close partnership with Product Management, Engineering, Design, User Research, Sales, Marketing, Finance, and other Microsoft organizations, you will influence senior leaders, align stakeholders around complex technical tradeoffs, and deliver innovations that improve learner outcomes, close skill gaps, accelerate proficiency, and connect skilling investments to measurable business impact. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees, we bring a growth mindset, innovate to empower others, and collaborate to achieve our shared goals. Every day, we build on our values of respect, integrity, and accountability to foster an inclusive culture 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 3+ 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 5+ 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 7+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Nice To Haves

10+ years of experience in data science, product/journey analytics, causal inference, and user behavioral modeling, at enterprise-scale.
Experience building or operationalizing learner graphs, knowledge graphs, or partner/field skilling graphs using graph intelligence techniques.
Experience in competency modeling, skill taxonomies, partner skilling analytics, certification readiness, or learning-based capability frameworks.
Experience with LLMs, NLP, multi-modal embeddings, agentic AI, and RAG/GraphRAG systems.
Experience with Fabric, Synapse, ADX, Delta Lake, ADF, Databricks, Snowflake or modern enterprise data architectures.
Experience leading cross-functional teams (data science, data engineering, software engineering, PM) in delivering end-end learning analytics platforms.
Executive communication skills with experience influencing C-level or VP-level decisions.
Experience building production-scale agentic AI solutions.
Experience building AI-ready solutions to accelerate insight efficiency and validation.

Responsibilities

Set technical direction, drive innovation across ambiguous and high-impact problems, and lead cross-organization initiatives from scientific formulation through production deployment.
Raise the bar for technical rigor, scalable engineering, responsible AI, and measurable business impact.
Architect and deliver production-scale agentic solutions that continuously measure learner progress and proficiency, personalize content and learning pathways using behavioral and contextual signals, and automate the generation, validation, and delivery of trusted insights across learner, product, and business audiences.
Define and operationalize enterprise-scale measurement methodologies for learner journeys, content quality, certification readiness, and proficiency progression, enabling optimized learning pathways across individuals, organizations, partners, and field roles.
Build and evolve skill graphs, taxonomies, competency models, and readiness frameworks that represent relationships among content, modalities, skills, certifications, and learning pathways, and power agentic measurement and personalization at scale.
Establish rigorous evaluation and observability frameworks for predictive, adaptive, and agentic learning systems, including reliability, bias, uncertainty, safety, quality, and outcome-based performance in production.
Set the experimentation and causal-measurement strategy for skilling outcomes, applying A/B testing, counterfactual analysis, causal inference, longitudinal cohort methods, and early-indicator modeling to guide product and investment decisions.
Partner with Product Engineering, Product Management, Sales, Marketing, Finance, and Global Skilling leaders to shape platform architecture, data and telemetry strategy, and reusable capabilities that scale trusted self-service and agent-generated insights across organizational boundaries.
Serve as a technical leader for the data science community by setting scientific and engineering standards, incubating novel methods and agentic capabilities, leading complex initiatives through production impact, mentoring scientists, and raising the organization-wide bar for innovation and execution.
Embody Microsoft culture and values.