Associate Director - AI/ML Data Scientist

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Overview We're looking for a Senior Data Scientist to lead the development of advanced analytics, machine learning, and AI capabilities that turn governed workforce data into actionable intelligence. In this role, you'll design predictive, probabilistic, and explanatory models that strengthen the accuracy and reliability of Lilly's People Intelligence platform — the enterprise capability that turns workforce data into decision-ready insight for HR leaders and the business.

Advanced Analytics & Data Science Design, validate, and operationalize statistical, machine-learning, and AI models for workforce use cases Develop predictive capabilities spanning attrition, talent risk, employee experience, hiring, mobility, skills, organizational health, and workforce planning Apply regression, classification, clustering, forecasting, causal inference, NLP, anomaly detection, and scenario modeling as appropriate Translate ambiguous workforce questions into clear analytical problems, hypotheses, methods, and measurable outcomes AI-Enabled People Intelligence Develop analytical logic for directional insights, risk signals, probabilistic guidance, and recommended follow-up questions Create and validate reusable AI skills, analytical workflows, prompts, and reasoning frameworks Build semantic models and drive Fabric architecture to be AI ready Evaluate the factual accuracy, analytical validity, consistency, and business usefulness of AI-generated responses Integrate models into Fabric Data Agents, Power BI, MCP, and other approved enterprise experiences Measurement & Governance Establish standards for validation, documentation, monitoring, explainability, retraining, and retirement Define performance measures, confidence levels, thresholds, and evaluation frameworks Identify bias, fairness, privacy, and unintended-consequence risks; partner with governance, privacy, legal, ER, and responsible-AI teams Communicate assumptions, limitations, and uncertainty, and maintain reproducible analytical methods Consulting & Technical Leadership Serve as a senior analytical advisor to HR leaders, HRBPs, Centers of Excellence, and product owners Distinguish descriptive findings, correlations, predictions, and causal conclusions in decision-oriented language Convert high-value analyses into reusable models, metrics, semantic-model enhancements, AI skills, or enterprise products Coach analysts and technical team members in advanced analytical methods Key Deliverables Predictive and causal workforce models — attrition/flight-risk forecasting, hiring-funnel and mobility prediction Governed semantic data models — reusable, self-service-ready data models spanning workforce, survey, and talent domains Generative AI-enabled employee-experience and text-analytics capabilities — sentiment/theme extraction from check-in notes, Pulse, and Leadership Compass verbatims Model accuracy, testing, and response-evaluation frameworks — validation pipelines benchmarking model outputs before production release Model-monitoring, model-governance, and responsible-AI standards — drift detection, bias/fairness checks, documented model lineage Reusable ML pipelines, feature stores, and data-science assets — production-grade, shared across workforce use cases rather than rebuilt per project