• Establish and lead enterprise-wide data management programs that ensure safe, compliant, high-quality data and AI. • Oversee Data Governance, AI Governance, and Data + AI Stewardship, serving as the central authority on policies, forums, and controls across R&D, Lab Operations, Commercial, and SG&A domains. • Lead the Data & AI Governance Council through advocacy and a well thought data management strategy. • Define enterprise data management strategy and operating model and ensure that it is executed consistently across the enterprise. • Chair and operationalize the AI & Data Governance Council, driving decision-making and accountability across legal, regulatory, compliance, IT, security, engineering, and product. • Lead a federated stewardship model, ensuring business units own data while governance enforces consistency and compliance. • Establish governance forums (steering committees, working groups, architecture boards) with clear outcomes. • Build and drive adoption of 360° master/reference datasets (e.g., Case360, Patient 360, Provider 360, Billing 360) and ensure they are maintained as sources of truth for analytics and AI. • Partner with engineering teams to build interoperable standards that can be used to connect domain datasets to create longitudinal data products. • Define and enforce enterprise data governance policies, ensuring consistency in data definitions, lineage, and stewardship across all domains. • Build and manage enterprise data catalogs and metadata services to make data discoverable, trustworthy, and reusable across the organization. • Establish and operate data quality frameworks with validation rules, anomaly detection, and automated testing to ensure accuracy, completeness, and timeliness. • Embed data quality checks and lineage tracking directly into data and AI pipelines so that governance guardrails can be adopted without friction. • Develop AI use case risk management framework (RMF) to evaluate AI use cases from a governance, regulatory, medical, privacy, security, and risk standpoint. • Build and maintain an AI risk register and incident response plan for all AI use cases. • Develop governance policies (privacy, security, quality, fairness, integrity) aligned to HIPAA, CLIA, FDA, GDPR, and emerging AI regulations. • Translate policies into technical implementations by embedding controls into ETL pipelines, feature stores, and model registries. • Build and execute agentic automation processes and associated guardrails to enable business process automation. • Build documentation and process to ensure agent accountability through change history, audit logs, versioning etc. • Track external regulatory trends and industry standards (e.g., NIST AI RMF, EU AI Act, FDA AI/ML guidance).