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

• 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 integration.

• Track and manage vendor AI risks through standardized assessments, approvals, and monitoring processes.

Qualifications

Required:

• 15+ years in Data or AI governance with at least 7+ in leadership roles.

• Excellent stakeholder management and executive communication skills.

• Proven track record of building and leading enterprise-wide governance programs, councils, and stewardship networks that span both data governance (catalogs, lineage, quality) and AI/ML governance (model risk, bias/fairness, explainability, monitoring).

• Deep understanding of healthcare regulatory frameworks impacting AI: HIPAA, CLIA, FDA, GDPR, and regulations (EU AI Act, NIST AI RMF, Japan AI Promotion Act).

• Experience with LLM/GenAI governance implementation as per common regulatory frameworks: prompt logging, bias testing, guardrails, explainability.

• Experience in vendor AI/ML evaluation and governance, including due diligence of third-party AI/ML tools and platforms.

Preferred:

• Certifications in data governance, privacy, or risk management (DAMA CDMP, CIPP, CRISC)

• Advanced degree (MS/PhD) in Computer Science, AI/ML, engineering or related field.

• Experience driving AI change management and building a culture of responsible AI adoption (training, awareness, champion networks).

• Proven experience embedding governance in code (pipelines, registries, CI/CD).