VP, Enterprise Responsible AI & Data Quality Assurance Lead

Job Classification:

Technology - Data Analytics & Management

Are you interested in building capabilities that enable the organization with innovation, speed, agility, scalability, and efficiency? Our Data & AI organization takes great pride in a culture where modernization, strong governance, and responsible innovation are built into how we work. When you join our organization, you’ll unlock an exciting and impactful career while helping teams across the enterprise adopt data and AI tools safely, consistently, and at scale.

Your Team

The Enterprise Responsible AI & Data Quality Assurance Lead is accountable for establishing and operating top-down oversight of data quality and responsible AI across the enterprise. This role defines the governance cadence, control standards, measurement framework, and executive reporting needed to ensure AI products and the data that powers them are safe, compliant, high quality, and fit for purpose. The Lead partners with business and technology leaders to drive adoption of guardrails, transparency of risks and exceptions, and measurable improvement through enterprise dashboards, KPIs, and prioritized remediation.

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

Here is What You Can Expect on a Typical Day

• Own the enterprise measurement strategy for Responsible AI and Data Quality, including definitions, thresholds, KPI/OKR taxonomy, and scorecards for AI products, models, and critical data products.
• Design and operate executive-level dashboards and reporting that provide transparent, repeatable visibility into compliance with Responsible AI policy, data quality health, model risk signals, exceptions, and remediation progress across the enterprise.
• Establish and maintain the assurance operating model (controls, testing procedures, evidence requirements, and audit-ready documentation) for data quality and AI governance guardrails, aligned to internal policies and external standards (e.g., NIST AI RMF, ISO/IEC, GDPR/CCPA where applicable).
• Lead governance cadence and decisioning forums (e.g., working groups and leadership councils), including agenda setting, risk/issue intake, prioritization, documented decisions, and escalation paths for policy exceptions and material risk findings.
• Define and execute technical reviews and assurance checkpoints for AI products (pre-release and in-production), including entry/exit criteria, independent challenge, and sign-off artifacts that demonstrate adherence to standards and controls.
• Partner with product, engineering, MLOps, data platform, legal, compliance, and risk teams to embed data quality and responsible AI requirements into product delivery processes, ensuring clear ownership, measurable controls, and scalable implementation patterns.
• Represent the Responsible AI and Data Quality assurance function in enterprise AI governance forums; bring forward material risks, trends, and escalations, and ensure outcomes are tracked through to closure.
• Lead triage, incident response, and remediation governance for data quality and AI-related issues (including severity classification, root cause analysis, control fixes, and executive communications), in partnership with RAI and DQ operating teams.
• Drive the enterprise roadmap for Data Quality and Responsible AI maturity, including capability gaps, prioritized investments, training/adoption enablement, and measurable outcomes reported to senior leadership.
• Deliver the process assurance needed for governance and policy development for Data Quality and Responsible AI.

The Skills and Expertise You Bring:

• Deep expertise in Data Quality frameworks (dimensions, rules, profiling, controls, monitoring) and enterprise data governance operating models.
• Responsible AI expertise across policy-to-implementation, including fairness, explainability, robustness, privacy, and human oversight; familiarity with frameworks/tools (e.g., NIST AI RMF, AIF360, Fairlearn, LIME, SHAP).
• Expertise in machine learning, deep learning, and AI model deployment
• Hands-on experience with dashboarding tools (e.g., Power BI, Tableau, Streamlit, Dash, Grafana) for AI and data reporting
• Demonstrated ability to create executive-ready reporting (board/committee-level), including clear narrative of risk, trends, exceptions, and remediation; strong stakeholder management and influencing skills.
• Solid understanding of data quality assurance, data governance, and data engineering best practices
• Proven ability to implement monitoring, auditing, and dashboarding solutions for AI systems
• Experience designing and operating control frameworks (testing, evidence, issue management) in partnership with risk, compliance, audit, or model risk m