Director, Data and AI Governance

Visa•Foster City, CA

1d•$200,000 - $320,000•Hybrid

About The Position

The Director; Data and AI Governance serves as the accountable governance representative for assigned business functions, helping them use data and artificial intelligence responsibly, effectively, and in accordance with applicable policies, standards, controls, and regulatory requirements. Acting as a trusted partner and conduit between the business and central policy and governance teams, the they provide practical, risk-based guidance across the lifecycle of data and AI initiatives. The role combines governance review, control validation, credible challenge, record quality, and executive advice with responsibility for improving how governance operates. The successful candidate will help standardize governance inputs, develop AI-enabled stewardship agents, and redesign workflows that use agentic capabilities with appropriate human oversight and accountability.

Requirements

10 or more years of work experience with a bachelor’s degree or at least 8 years of work experience with an Advanced Degree (e.g. Masters/ MBA/JD/MD) or at least 3 years of work experience with a PhD.
Bachelor’s degree or equivalent practical experience.
Relevant experience in data governance, AI governance, risk management, privacy, compliance, cybersecurity, model risk, technology governance, product governance, or a related field, including responsibility for reviewing initiatives and advising stakeholders.
Working knowledge of data and AI lifecycle concepts and associated governance risks.
Experience interpreting policies, standards, controls, or regulatory requirements; validating implementation and supporting evidence; and coordinating remediation when gaps are identified.
Strong analytical and problem-solving skills, with the ability to evaluate complex or incomplete information.
Strong written and verbal communication skills.
Ability to translate technical and governance requirements into clear, practical guidance for non-specialist and executive audiences.
Experience working across business, product, technology, legal, privacy, security, risk, or compliance teams.
Strong organizational skills and the ability to manage multiple reviews, actions, and deadlines.
Demonstrated judgment and influence to balance executive priorities against risk-based outcomes, provide credible challenge, and escalate or recommend pausing activity when requirements are not met.
Hands-on experience reviewing, developing, testing, or governing machine learning, predictive AI, generative AI, or agentic AI solutions across their lifecycle.
Familiarity with data inventories, model inventories, governance workflow tools, risk and control systems, or data catalogs.
Knowledge of data classification, metadata management, lineage, retention, access controls, data quality, model validation, or ongoing monitoring.
Familiarity with relevant data protection, AI, technology, or financial-services regulatory frameworks.
Experience supporting control testing, quality assurance, audit, regulatory examinations, or issue remediation.
Experience standardizing governance inputs, evidence, taxonomies, or decision criteria and developing reusable guidance, training, playbooks, or automated workflows.
Experience contributing to process or organizational transformation involving AI-enabled or agentic workflows, including effective human oversight and adoption.

Nice To Haves

12 or more years of work experience with a bachelor’s degree or 8-10 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 6+ years of work experience with a PhD.
Relevant professional certification in data governance, privacy, risk, security, compliance, audit, or AI governance.

Responsibilities

Serve as the accountable governance representative for assigned business functions, helping ensure that data and AI activities are understood, risks are managed, controls are in place, and governance requirements are met within established service levels.
Review assigned data and AI initiatives for submission completeness and compliance; identify privacy, security, data quality, model, legal, regulatory, ethical, and operational risks; coordinate subject-matter expert review; and support risk acceptance, exceptions, and escalation.
Provide practical, risk-based guidance to initiative owners and advise executives on governance risks, options, trade-offs, and required controls, including escalating or recommending that activity be paused or modified when requirements are not met.
Ensure accurate, complete, and high-quality records for data and AI systems, models, and use cases, including ownership, classification, metadata, lineage, access, retention, quality, controls, validation evidence, and lifecycle status.
Validate that required controls are implemented, operating effectively, and supported by appropriate evidence; challenge unsupported assertions; coordinate remediation of identified gaps through closure; and perform quality assurance and peer review of governance decisions.
Design, develop, test, and continuously improve AI-enabled stewardship agents that augment governance activities while maintaining appropriate human oversight, accountability, and controls.
Drive standardization of governance inputs, evidence, taxonomies, and decision criteria to improve consistency, enable automation, and support scalable agentic workflows.
Contribute to organizational transformation initiatives that integrate agentic capabilities into governance processes, including redesigning workflows, defining effective human-agent interaction, and supporting responsible adoption.
Build governance readiness within the business through training, coaching, awareness activities, and proactive engagement with initiative owners and stakeholders; communicate relevant policy, process, and regulatory updates; and participate in governance forums and communities of practice.
Act as the business-level conduit to central Data & AI Policy and Governance teams by contributing business insight to policies, standards, controls, and guidance; translating requirements into practical actions; and communicating implementation challenges and emerging risks.