VP, Enterprise Responsible AI and Data Quality Assurance Operations

Vice President, Enterprise Responsible AI (RAI) and Data Quality (DQ) Assurance Operations

Are you interested in building capabilities that enable the organization with innovation, speed, agility, scalability, and efficiency? The Global Technology team takes great pride in our culture where digital transformation is built into our DNA! When you join our organization at Prudential, you'll unlock an exciting and impactful career all while growing your skills and advancing your profession at one of the world's leading financial services institutions.

As the Vice President, Enterprise Responsible AI (RAI) and Data Quality (DQ) Assurance Operations at Prudential Financial, you will lead the enterprise operating model and control plane for RAI and DQtranslating policy and standards into actionable controls, decision rights, automation, and measurable outcomes across business units and technology teams. In partnership with the VP, Responsible AI and QA Leader, you will establish centralized oversight mechanisms and ensure governance is executed in the flow of delivery across the AI and data lifecycles.

In this global role, you will run the centralized governance engine for Responsible AI and Data Qualitydefining enterprise-wide intake, risk-tiering, review, approval, and escalation workflows; setting expectations for documentation, testing, and monitoring; and implementing policy-as-code patterns where feasible to automate control checks and evidence capture across ModelOps/DataOps platforms.

Your leadership will strengthen Prudential's ability to scale AI safely by operationalizing continuous monitoring for model performance, fairness, drift, and stabilitypaired with modern data observability for completeness, accuracy, timeliness, and lineage. You will establish an enterprise issue, incident, and exception management approach (ownership, SLAs, remediation, and audit-ready evidence) and provide senior leaders transparent reporting on risk posture, control effectiveness, and compliance status.

In essence, as VP of Enterprise RAI and DQ Assurance Operations, you will design and run Prudential's end-to-end RAI/DQ operations and control planeensuring consistent enforcement of standards, measurable control coverage, and independent challenge so AI and data products are delivered in alignment with policy, risk appetite, and evolving regulatory expectations.

Location: Newark, NJ hybrid (minimum 3 days/week in office)

Here is What You Can Expect on a Typical Day

• Establish and enforce an enterprise Responsible AI and Data Quality governance execution model and control plane (controls, decision rights, operating cadence, control testing, and issue/exception management), in coordination with the VP, RAI Governance & Policy.
• Own the centralized workflows, tooling, and integrations required for enterprise oversightenabling model and use-case inventory, control mapping, required evidence capture, attestations, and continuous monitoring (leveraging ModelOps and adjacent platforms).
• Develop and maintain governance frameworks, mandatory standards, control libraries, playbooks, templates, and audit-ready documentation that teams must follow to meet Responsible AI and Data Quality requirements efficientlyembedding controls into delivery through repeatable patterns and guidance.
• Lead cross-enterprise governance rhythms (intake, risk-tiering, review boards, control gates, independent challenge, and escalation) to drive consistent adoption of RAI and DQ controls across business, technology, and analytics teams.
• 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 (including critical data elements) and executing independent assurance (rules-based testing, sampling, and challenge) supported by modern data observability and lineage/provenance.
• Establish enterprise DQ monitoring and issue/incident management with clear ownership, SLAs, escalation paths, root-cause analysis expectations, and audit-ready evidence of detection, triage, remediation, and validation.
• Integrate data quality controls into AI lifecycle governance gates (intake, validation, deployment, and monitoring) to ensure models rely on fit-for-purpose data and featureswith documented lineage, provenance, label/ground-truth quality where applicable, and defined acceptance criteria for data fitness.
• Manage the RAI/DQ operations tooling roadmap with product and engineering partnersdriving integration across model registries, evaluation frameworks, monitoring/observability, metadata and lineage, case management, and reporting to support scalable control-plane execution.
• Build and lead role-based training and enablement that empowers teams to execute RAI and DQ practices effectivelyfocusing on 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 into Prudential's operational model and control plane.
• Drive continuous improvement to raise the maturity, automation, and scalability of RAI/DQ operationsreducing delivery friction while strengthening control effectiveness and audit readiness.
• Develop strong relationships with business, technology, risk, compliance, and legal partnersensuring shared understanding of requirements, clear ownership, 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 involved in AI and data delivery and consistent execution of governance decisions.

The Skills & Expertise You Bring

• A minimum of 10 years of experience in a leadership role with a focus on AI governance, policy, risk management, ethics, or a related discipline, ideally within complex, global, and regulated environments.
• Strong understanding of AI/ML development lifecycles, model operations, and the technical processes required to support 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, and/or data observability) in regulated environments.
• Demonstrated experience operationalizing complex technical programs across large, federated organizations.
• Proven 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 science, applied mathematics, AI/ML, engineering, or a related field preferred.