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 R&D, Lab Operations, Commercial, and SG&A 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