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Apply Now →About This Role
Verisk Analytics is seeking a Director, AI Governance Lead to advance the responsible, ethical, transparent, and well\-controlled use of artificial intelligence across the enterprise as Verisk operationalizes the AI Governance Platform and matures its AI Governance Workflow.
Reporting to the VP, Data Strategy \& Governance within the Chief Data Office, this leader will own the internal operating model for AI use case intake, risk assessment, stakeholder review, approvals, inventory, reporting, and ongoing monitoring.
The Director will serve as Verisk's customer\-side owner for AI Governance platform readiness, initial deployment, and business\-as\-usual adoption. This includes coordinating AI Governance Advisory workshops and deliverables, aligning business and risk stakeholders, managing platform data quality, and translating implementation outputs into durable governance practices.
This role will partner closely with Legal, Business, Product, Technology, Data Science, Privacy, Compliance, Information Security, Data Governance, Procurement, Vendor, Enterprise Risk, Audit, and executive stakeholders. The successful candidate will be comfortable moving from board\-ready governance materials to hands\-on workflow details, user enablement, and issue resolution
AI Use Case Intake, Triage, and Governance Workflow
- Own and manage the enterprise AI use case intake, triage, and governance workflow across Verisk.
- Serve as the primary governance contact for teams submitting AI use cases to the AI Governance Board for approval.
- Guide requesting teams through required documentation, registry metadata, risk assessment, stakeholder review, control requirements, approval steps, and ongoing governance obligations.
- Evaluate AI use cases for completeness, business purpose, data sensitivity, customer or regulatory impact, model/system risk, third\-party involvement, and readiness for governance review.
Track AI use cases from intake through triage, review, approval, implementation, monitoring, remediation, and closure.
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AI Governance Implementation and Platform Ownership
- Serve as Verisk's business owner and day\-to\-day operational lead for the AI Governance Platform.
- Lead the internal side of the AI Governance Advisory engagement, including readiness activities, workshop participation, documentation requests, stakeholder coordination, deliverable review, and follow\-up actions.
- Maintain AI use case inventory, workflows, intake forms, questionnaires, assessments, risk classifications, stakeholder reviews, approval records, evidence, controls, remediation items, and reporting in the AI Governance Platform.
Partner with the AI Governance tool vendor and internal teams to configure and validate workflow stages, questionnaires, automations, conditional routing, risk types, risk scenarios, policy packs, controls, reports, and self\-service intake capabilities.
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AI Governance Board and Stakeholder Operating Model
- Administer and coordinate AI Governance Board operations, including agenda planning, meeting scheduling, presentation preparation, decision logs, minutes, record keeping, and follow\-up actions.
- Translate the stakeholder operating model into clear roles, responsibilities, review expectations, escalation paths, and handoffs at both program and use\-case levels.
- Prepare AI use cases for board review by developing concise summaries, risk profiles, issue logs, stakeholder input, open questions, and recommended decision points.
- Track board decisions, conditions of approval, exceptions, remediation items, escalations, unresolved risks, and required evidence.
Coordinate with Legal, Privacy, Compliance, Information Security, Data Governance, Procurement, Vendor Management, Third\-Party Risk, Enterprise Risk, Technology, Audit, and business stakeholders to ensure reviews are timely and complete.
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Responsible AI Governance Framework
- Support the AI Governance Board and cross\-functional teams to implement Verisk’s AI governance policies, standards, procedures, templates, and guidance.
- Support Verisk's global AI governance strategy and help ensure AI systems are developed, deployed, and monitored in a responsible and transparent manner consistent with Verisk's Ethical AI Policy.
- Maintain AI governance framework components, including intake standards, risk assessment criteria, questionnaire logic, inventory requirements, stakeholder routing, approval standards, reporting processes, and control documentation.
Partner with Legal, Compliance, Privacy, Information Security, Procurement, and Third\-Party Risk on policy mapping, vendor AI addenda inputs, policy\-pack interpretation, and control expectations, while ensuring formal legal and policy decisions remain with the appropriate owners.
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Reporting, Metrics, and Executive Communication
- Develop recurring and ad hoc AI governance reporting for the CDO office, AI Governance Board, senior leadership, risk committees, business stakeholders, and other internal audiences.
- Translate AI Governance reporting outputs into stakeholder\-ready views that show governance status, reviewer actions, pipeline trends, risk tiers, open issues, and executive decision points.
- Track and report key metrics such as AI use case volume, review status, approval cycle time, risk tiering, business unit participation, stakeholder review status, remediation items, policy exceptions, audit findings, and board decisions.
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Information Systems, Business, Risk Management, Law, Public Policy, or a related field, or equivalent practical experience.
- 10\+ years of relevant professional experience in AI governance, data governance, model governance, technology governance, risk management, audit, compliance, privacy, data science operations, product governance, or a related field.
- 5\+ years of experience working within governance, compliance, ethics, risk, privacy, technology controls, or related disciplines, preferably involving AI, machine learning, data products, analytics, or emerging technologies.
- Proven experience leading cross\-functional governance programs, workflow implementations, platform rollouts, operating\-model changes, or enterprise control processes.
- Strong understanding of AI, machine learning, generative AI, automated decisioning, data science workflows, AI system lifecycle risks, and responsible AI principles.
- Demonstrated experience coordinating complex cross\-functional reviews involving Legal, Privacy, Compliance, Risk, Information Security, Technology, Data, Product, Procurement, Third\-Party Risk, and business stakeholders.
- Experience preparing executive\-level presentations, governance materials, dashboards, status reports, training materials, and decision documents.
- Ability to influence and build credibility across a matrixed, global organization without relying solely on formal authority.
- IAPP’s AIGP certification is preferred.
\#LI\-EA1
For over 50 years, Verisk has been the leading data analytics and technology partner to the global insurance industry by delivering value to our clients through expertise and scale. We empower communities and businesses to make better decisions on risk, faster.
At Verisk, you'll have the chance to use your voice and build a rewarding career that's as unique as you are, with work flexibility and the support, coaching, and training you need to succeed.
For the eighth consecutive year, Verisk is proudly recognized as a Great Place to Work® for outstanding workplace culture in the US, the fourth consecutive year in the UK, Spain, and India, and the second consecutive year in Poland. In addition, we’ve been recognized by The Wall Street Journal as one of the Best\-Managed Companies and by Forbes as a World’s Best Employer, testaments to the value we place on workplace culture.
We’re 7,000 people strong. We relentlessly and ethically pursue innovation. And we are looking for people like you to help us translate big data into big ideas. Join us and create an exceptional experience for yourself and a better tomorrow for future generations.
Verisk Businesses
Underwriting Solutions — provides underwriting and rating solutions for auto and property, general liability, and excess and surplus to assess and price risk with speed and precision
Claims Solutions — supports end\-to\-end claims handling with analytic and automation tools that streamline workflow, improve claims management, and support better customer experiences
Property Estimating Solutions — offers property estimation software and tools for professionals in estimating all phases of building and repair to make day\-to\-day workflows the most efficient
Specialty Business Solutions — provides an integrated suite of software for full end\-to\-end management of insurance and reinsurance business, helping companies manage their businesses through efficiency, flexibility, and data governance
Catastrophe and Risk Solutions — provides risk modeling solutions to help individuals, businesses, and society become more resilient to catastrophic events.
Marketing Solutions — delivers data and insights to improve the reach, timing,relevance, and compliance of every consumer engagement
Life Insurance Solutions – offers end\-to\-end, data insight\-driven core capabilities for carriers, distribution, and direct customers across the entire policy lifecycle of life and annuities for both individual and group.
Verisk Maplecroft — provides intelligence on sustainability, resilience, and ESG, helping people, business, and societies become stronger
Verisk Analytics is an equal opportunity employer.
Verisk invests in a benefits package for all employees that includes the following: Health Insurance, a Retirement Plan, Disability benefits, and a Paid Time Off program. We offer a competitive total rewards package that includes base salary determined based on role, experience, skill set, and location.
All members of the Verisk Analytics family of companies are equal opportunity employers. We consider all qualified applicants for employment without regard to race, religion, color, national origin, citizenship, sex, gender identity and/or expression, sexual orientation, veteran's status, age or disability. Verisk’s minimum hiring age is 18 except in countries with a higher age limit subject to applicable law.
https://www.verisk.com/company/careers/
Unsolicited resumes sent to Verisk, including unsolicited resumes sent to a Verisk business mailing address, fax machine or email address, or directly to Verisk employees, will be considered Verisk property. Verisk will NOT pay a fee for any placement resulting from the receipt of an unsolicited resume.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Verisk, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Verisk AI Hiring
Verisk has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jersey City, NJ, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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