VP, Enterprise AI and Data Quality Assurance Operations

Location:

Newark, NJ (hybrid, minimum 3 days per week in office)

The Vice President, Enterprise Responsible AI (RAI) and Data Quality (DQ) Assurance Operations at Prudential Financial leads the enterprise operating model and control plane for RAI and DQ. Translating policy and standards into actionable controls, decision rights, automation and measurable outcomes, this global role drives governance across AI and data life cycles, ensuring compliance with regulatory expectations and risk appetite.

• Establish and enforce an enterprise Responsible AI and Data Quality governance execution model and control plane, coordinating with the VP, RAI Governance & Policy.
• Own the centralized workflows, tooling, and integrations required for enterprise oversight, enabling model and use‑case inventory, control mapping, evidence capture, attestations, and continuous monitoring.
• Develop and maintain governance frameworks, mandatory standards, control libraries, playbooks, templates, and audit‑ready documentation to embed controls into delivery through repeatable patterns.
• Lead cross‑enterprise governance rhythms (intake, risk‑tiering, review boards, control gates, independent challenge, escalation) to drive consistent adoption of RAI and DQ controls.
• Define and publish enterprise KPIs and dashboards spanning compliance, risk posture, control coverage/effectiveness, exceptions, incidents, and remediation progress; deliver actionable reporting to senior stakeholders and governance forums.
• Lead the Enterprise Data Quality Assurance (DQA) function by defining and running top‑down quality standards and controls for critical data, executing independent assurance via rules‑based testing, sampling, and challenge.
• Establish enterprise DQ monitoring and issue/incident management with clear ownership, SLAs, escalation paths, root‑cause analysis expectations, and audit‑ready evidence.
• Integrate data quality controls into the AI lifecycle governance gates to ensure models rely on fit‑for‑purpose data and features with documented lineage, provenance, and acceptance criteria.
• Manage the RAI/DQ operations tooling roadmap with product and engineering partners, driving integration across model registries, evaluation frameworks, monitoring/observability, metadata, lineage, case management, and reporting.
• Build and lead role‑based training and enablement, providing practitioner playbooks, templates, office hours, and hands‑on support.
• Stay current on emerging tools, methods, and regulatory expectations (including GenAI/LLM evaluation, monitoring, and documentation patterns) and integrate improvements.
• Drive continuous improvement to raise the maturity, automation, and scalability of RAI/DQ operations, reducing delivery friction while strengthening control effectiveness and audit readiness.
• Develop strong relationships with business, technology, risk, compliance, and legal partners to ensure shared understanding of requirements and a compelling value proposition for RAI and DQ controls.
• Represent the RAI/DQ Operations control plane in cross‑enterprise forums, ensuring alignment across stakeholders.

• Minimum 10 years of leadership experience focused on AI governance, policy, risk management, ethics, or a related discipline, ideally within complex, global, and regulated environments.
• Strong understanding of AI/ML development life cycles, model operations, and technical processes for scalable AI delivery.
• Demonstrated leadership in enterprise data quality governance and assurance, including defining DQ standards/controls, overseeing testing and monitoring, and driving remediation across federated data owners.
• Working knowledge of data quality and data governance practices and tooling (DQ rules, metadata management, lineage, stewardship, MDM/reference data, or data observability) in regulated environments.
• Proven experience operationalizing complex technical programs across large, federated organizations.
• Ability to lead cross‑functional teams and influence technical and non‑technical stakeholders.
• Exceptional communication, collaboration, and problem‑solving skills.
• Advanced degree in computer science, data…