Director, Data and AI Governance

Role Overview As the Director of Data AI Governance, you will establish and lead enterprise-wide data management programs that ensure safe, compliant, high-quality data and AI. You will oversee Data Governance, AI Governance, and Data + AI Stewardship, serving as the central authority on policies, forums, and controls across RD, Lab Operations, Commercial, and SGA domains. You will lead the Data AI Governance Council through advocacy and a well thought data management strategy. This role goes beyond policies into technical governance — it requires experience of how to build frameworks, deploy controls in code, and integrate governance into engineering delivery. The role spans all dimensions of governance, including: quality, privacy, security, agentic automation, AI risk management, bias/fairness testing, evals, and vendor AI evaluation. Key Responsibilities Governance Council Operating Model 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. Data Governance Quality 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. AI Policy Engineering Implementation 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 CI/CD workflows for ML/GenAI models Prompt orchestration and output logging for LLMs Bias/fairness testing, drift detection, explainability dashboards AI Risk Automation 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) AI Change Management Vendor Governance Lead AI change management initiatives, including training programs, awareness campaigns, and a network of governance champions to drive adoption of best practices. Partner with Corporate Communications to cascade governance updates, AI guardrails, and usage guidelines across all levels of the organization. Develop and enforce vendor and third-party AI evaluation frameworks, assessing external AI tools for governance, data security, model risk, and compliance posture before integ ... (truncated, view full listing at source)