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 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