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

• CI/CD workflows for ML/GenAI models

• Prompt orchestration and output logging for LLMs

• Bias/fairness testing, drift detection, explainability dashboards

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) 

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.

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.

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).

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

• CI/CD workflows for ML/GenAI models

• Prompt orchestration and output logging for LLMs

• Bias/fairness testing, drift detection, explainability dashboards

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) 

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.

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.

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).