Principal Data Scientist

As a Principal Data Scientist, you will set technical direction, drive innovation across ambiguous and high-impact problems, and lead cross-organization initiatives from scientific formulation through production deployment. You will raise the bar for technical rigor, scalable engineering, responsible AI, and measurable business impact through the following responsibilities: 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. 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. 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-to-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.