Director, AI Governance

Kyndryl

Director of AI Governance at Kyndryl utilizing data to drive strategic decisions and manage AI compliance. Overseeing governance frameworks and collaborating with cross-functional teams for responsible AI usage.

Posted 7/3/2026full-timeRemote • New York, Texas • 🇺🇸 United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in establishing and operationalizing data governance frameworks, ensuring compliance with global data regulations and ethical AI usage. Proven ability to lead cross-functional governance programs and communicate complex concepts to executive stakeholders.

Highest-signal resume keywords

Data GovernanceAI GovernanceData Quality ManagementRegulatory ComplianceCross-Functional Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data QualityMetadata ManagementData LineageAI Model Lifecycle GovernanceGovernance KPIs

Soft Skills

CollaborationCommunicationInfluence

Tools & Technologies

Low-Code AI ToolsGenerative AI Tools

Industry Keywords

EU AI ActNIST AI RMFData PrivacyData Stewardship

About the role

Key responsibilities & impact

Establish and operationalize a comprehensive governance framework covering data quality, privacy, lineage, and ethical AI usage across all enterprise systems and platforms.
Chair the Data Council and participate in the AI Governance Board to review and approve new data and AI use cases, ensuring alignment with enterprise standards and policies.
Manage enterprise data catalog, metadata standards, and stewardship processes to maintain clarity of ownership, lifecycle management, and access control.
Partner with the Chief Privacy Officer, Chief Information Security Officer, and Legal to ensure adherence to global data regulations, privacy laws, and AI governance mandates.
Define guardrails for citizen-led AI development, ensuring secure, compliant, and responsible use of low-code and generative AI tools across the organization.
Develop and track governance KPIs—such as data quality scores, privacy incidents, and model validation rates—to demonstrate accountability and progress.

Requirements

What you’ll need

10+ years of experience in data governance, information management, or AI governance within complex enterprise environments.
Deep understanding of data management disciplines, including data quality, metadata, privacy, and lineage frameworks.
Familiarity with AI model lifecycle governance, responsible AI principles, and regulatory frameworks (e.g., EU AI Act, NIST AI RMF).
Demonstrated success leading enterprise councils or cross-functional governance programs.
Strong collaboration skills with privacy, security, and compliance organizations.
Exceptional communication and influence skills with the ability to translate governance and AI concepts into clear executive-level insights.

Benefits

Comp & perks

Health insurance
Professional development opportunities
Remote work options