VP, Enterprise Responsible AI & Data Quality Assurance Lead

$239K - $359K Newark, NJ, US Senior AI Safety

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Skills & Technologies

Power BiPythonPytorchTableauTensorflow

About This Role

AI job market dashboard showing open roles by category

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 management functions.
  • Knowledge of regulatory standards and ethical requirements for AI (GDPR, CCPA, NIST, ISO)
  • Excellent problem\-solving, communication, and cross\-functional collaboration skills
  • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related fields
  • Experience managing complex AI projects and providing technical mentorship
  • Strong proficiency in Python, R, and AI libraries (e.g., TensorFlow, PyTorch, scikit\-learn)

\#LIhybrid

What we offer you:

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Prudential is required by state specific laws to include the salary range for this role when hiring a resident in applicable locations. The salary range for this role is from $239,700\.00 to $359,500\.00\. Specific pricing for the role may vary within the above range based on many factors including geographic location, candidate experience, and skills.* Market competitive base salaries, with a yearly bonus potential at every level.

  • Medical, dental, vision, life insurance, disability insurance, Paid Time Off (PTO), and leave of absences, such as parental and military leave.
  • 401(k) plan with company match (up to 4%).
  • Company\-funded pension plan.
  • Wellness Programsincluding up to $1,600 a year for reimbursement of items purchased to support personal wellbeing needs.
  • Work/Life Resources to help support topics such as parenting, housing, senior care, finances, pets, legal matters, education, emotional and mental health, and career development.
  • Education Benefit to help finance traditional college enrollment toward obtaining an approved degree and many accredited certificate programs.
  • Employee Stock Purchase Plan: Shares can be purchased at 85% of the lower of two prices (Beginning or End of the purchase period), after one year of service.

Eligibility to participate in a discretionary annual incentive program is subject to the rules governing the program, whereby an award, if any, depends on various factors including, without limitation, individual and organizational performance. To find out more about our Total Rewards package, visit Work Life Balance \| Prudential Careers. Some of the above benefits may not apply to part\-time employees scheduled to work less than 20 hours per week.

Prudential Financial, Inc. of the United States is not affiliated with Prudential plc. which is headquartered in the United Kingdom.

Prudential is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender identity, national origin, genetics, disability, marital status, age, veteran status, domestic partner status, medical condition or any other characteristic protected by law.

If you need an accommodation to complete the application process, please email [email protected].

If you are experiencing a technical issue with your application or an assessment, please email [email protected] to request assistance.

Salary Context

This $239K-$359K range is above the 75th percentile for AI Safety roles in our dataset (median: $195K across 14 roles with salary data).

Role Details

Company Prudential
Title VP, Enterprise Responsible AI & Data Quality Assurance Lead
Location Newark, NJ, US
Category AI Safety
Experience Senior
Salary $239K - $359K
Remote No

About This Role

This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.

The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.

Across the 3,824 AI roles we're tracking, AI Safety positions make up 0% of the market. At Prudential, this role fits into their broader AI and engineering organization.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

What the Work Looks Like

Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

Skills Required

Power Bi (5% of roles) Python (51% of roles) Pytorch (15% of roles) Tableau (4% of roles) Tensorflow (13% of roles)

Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.

Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.

Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

Compensation Benchmarks

AI Safety roles pay a median of $274,200 based on 51 positions with disclosed compensation. This role's midpoint ($299K) sits 9% above the category median. Disclosed range: $239K to $359K.

Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and Research Engineer ($260,000). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.

Prudential AI Hiring

Prudential has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Safety. Based in Newark, NJ, US. Compensation range: $267K - $359K.

Location Context

Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).

Career Path

Common paths into AI Safety roles include Software Engineer, Data Scientist, Data Analyst.

From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.

Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.

What to Expect in Interviews

AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.

When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

AI Hiring Overview

The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.

The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

The AI Job Market Today

The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 51 roles with disclosed compensation, the median salary for AI Safety positions is $274,200. Actual compensation varies by seniority, location, and company stage.
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
About 16% of the 3,824 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Prudential is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Safety positions include Senior Engineer, AI Architect, Engineering Manager, Principal Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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