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Verisk Analytics is seeking a Senior AI Governance Analyst to support the responsible, ethical, transparent, and well\-controlled use of artificial intelligence across the enterprise as Verisk operationalizes its AI Governance Platform and matures its AI Governance Workflow.
Reporting to the Director, AI Governance Lead within the Chief Data Office, this individual contributor will manage the day\-to\-day execution of AI use case intake, initial risk triage, stakeholder coordination, platform administration, AI Governance Board preparation, decision tracking, ongoing monitoring, reporting, and user enablement.
This person will help business, product, technology, and data science teams navigate governance requirements; ensure submissions are complete and routed to the appropriate reviewers; maintain accurate and audit\-ready records; and follow approvals, conditions, exceptions, and remediation items through closure.
This role will partner closely with Legal, Privacy, Compliance, Information Security, Data Governance, Procurement, Vendor Management, Third\-Party Risk, Enterprise Risk, Audit, Technology, Product, Data Science, and business stakeholders.
The successful candidate will be highly organized, comfortable multi\-tasking and prioritizing, able to understand and document AI system details, and a critical thinker skilled at translating governance requirements into practical steps for business and technical teams .
Acknowledging that AI governance is an evolving discipline, you will work to confirm that our global AI inventory is accurate, transparent, and in\-line with the dynamic landscape of AI regulation and measurement.
This role will be Hybrid in our Jersey City, NJ location.
AI Use Case Intake, Triage, and Workflow Operations
- Serve as an operational point of contact for teams submitting AI, machine learning, generative AI, automated decisioning, and third\-party AI use cases for governance review.
- Review submissions for completeness, clarity, and data quality, including business purpose, intended users, affected customers, data sources, model or solution type, third\-party involvement, deployment approach, human oversight, and potential regulatory or reputational impact.
- Apply established intake and triage criteria to support preliminary risk classification, identify required evidence, and route use cases to the appropriate Legal, Privacy, Compliance, Information Security, Data Governance, Procurement, Third\-Party Risk, and other reviewers.
- Guide requesting teams through documentation, risk assessments, stakeholder reviews, control requirements, approval steps, conditions of approval, monitoring obligations, and change or re\-review requirements.
- Manage the active case queue, follow up on missing information and overdue reviews, track service\-level expectations, identify bottlenecks, and escalate aging, high\-risk, or unresolved matters to the Director.
- Track AI use cases from initial intake through review, board decision, implementation, monitoring, remediation, periodic reassessment, and closure.
AI Governance Platform Administration and Data Quality
- Administer day\-to\-day records and workflows within the AI Governance Platform under the direction of the Director, AI Governance Lead.
- Maintain AI inventory records, intake forms, questionnaires, assessments, risk classifications, stakeholder reviews, approval records, evidence, controls, conditions, exceptions, remediation items, monitoring records, and reporting fields.
- Support platform configuration, user acceptance testing, release validation, workflow changes, conditional routing, notifications, dashboards, reports, user access, and self\-service capabilities.
AI Governance Board and Stakeholder Coordination
- Support AI Governance Board operations, including agenda planning, meeting scheduling, case prioritization, presentation preparation, reviewer follow\-up, minutes, decision logs, record keeping, and action tracking.
- Capture and communicate Board decisions, conditions of approval, exceptions, required controls, remediation commitments, re\-review triggers, and evidence requirements to use case owners and reviewers.
- Maintain audit\-ready evidence of submissions, assessments, reviews, approvals, exceptions, decisions, communications, and follow\-up actions.
Ongoing Monitoring, Conditions, and Remediation
- Track post\-approval conditions, exceptions, remediation plans to completion, periodic review dates, owner attestations, monitoring evidence, material changes, incidents, and re\-approval requirements.
- Escalate overdue, incomplete, or high\-risk items and support the Director in preparing issue summaries and recommended next steps for the AI Governance Board or other oversight groups.
- Assist with use case closure, archival, decommissioning, and inventory updates when AI systems are retired or no longer in scope.
Reporting, Metrics, and Continuous Improvement
- Develop recurring and ad hoc reports for the Director, Chief Data Office, AI Governance Board, senior leadership, risk committees, business stakeholders, and other internal audiences.
- Track and report metrics such as AI use case volume, review status, approval cycle time, aging, risk tier, business unit participation, reviewer turnaround, conditions, remediation items, policy exceptions, and Board decisions.
- Analyze workflow trends and bottlenecks, identify opportunities to simplify, automate or improve governance activities, and support implementation of approved process improvements.
- Bachelor's degree in Computer Science, Artificial Intelligence, Information Systems, Business, Risk Management, Compliance, Privacy, Public Policy, or a related field, or equivalent practical experience.
- Four to six years of relevant professional experience in AI governance, data governance, model governance, technology governance, risk management, compliance, privacy, audit, technology controls, data science operations, product governance, program management, or a related field.
- Hands\-on experience operating a structured intake, risk assessment, approval, inventory, issue\-management, remediation, or control\-evidence process in a complex organization.
- Comfortable with new technologies, some understanding of AI, machine learning, generative AI, automated decisioning, data science workflows , AI system lifecycle risks, and responsible AI principles.
- Ability to understand and document an AI use case’s business purpose, users, data, model or solution design, third\-party dependencies, human oversight, outputs, potential impacts, and control requirements.
- Experience coordinating cross\-functional reviews involving business, product, technology, data, Legal, Privacy, Compliance, Information Security, Risk, Procurement, or Audit stakeholders.
- Experience supporting enterprise AI governance platform implementation or GRC systems , workflow rollout, process redesign, or governance operating\-model change.
- Preferred: Attained or interest in attaining the AI Governance Professional (AIGP) certification
\#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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Verisk AI Hiring
Verisk has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Jersey City, NJ, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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