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Job Details
---------------
Location:
Columbus, OH
Category:
Data \& Analytics
Employment Type:
Full time, Hybrid
Job Ref:
R2625965\-174
VP Data Science \- GD04AE
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
The Hartford is hiring a Vice President, Applied AI – Employee Benefits \& Operations to define and lead the next generation of AI\-driven transformation across the business. This role is responsible for driving Applied AI and Data Science strategy, execution, and governance across enrollment, billing, customer service, and operational functions, delivering measurable improvements in growth, efficiency, accuracy, and customer experience. The leader will oversee a diverse portfolio of Machine Learning, Generative AI, Agentic AI, and predictive analytics solutions while influencing enterprise AI strategy, championing responsible AI practices, and building organizational capability to accelerate The Hartford’s AI\-powered future.
This role can have a Hybrid or Remote work schedule. Candidates who live near one of our office locations (Hartford, CT; Chicago, IL; Columbus, OH; Charlotte, NC) will have the expectation of working in an office 3 days a week. Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise. Candidates must be eligible to work in the US without company sponsorship.
Primary Job Responsibilities
- Own Applied AI strategy and integrated data science outcomes across Employee Benefits and Operations, including employer lifecycle, enrollment, billing, service delivery, and operational functions, ensuring alignment to enterprise AI priorities while tailoring execution to domain\-specific needs.
- Define domain\-level Applied AI strategy and influence enterprise AI direction through evidence\-based recommendations, technical insight, and cross\-functional alignment.
- Lead executive decision\-making across supported lines of business, driving trade\-offs across quality, risk, cost, scalability, and time\-to\-value for Applied AI and data science initiatives.
- Drive transformation of Employee Benefits and Operations processes through Applied AI and data science, including enrollment optimization, service automation, contact center intelligence, billing accuracy, and employer/member experience across the full lifecycle.
- Lead application and execution of the enterprise Applied AI operating model within domain scope, ensuring teams effectively operate within defined decision rights, engagement models, and delivery governance across multiple portfolios.
- Ensure consistent adherence to enterprise AI governance frameworks across portfolios, including application of evaluation, monitoring, model risk management, and responsible AI practices in alignment with enterprise standards and regulatory expectations.
- Set direction for evaluation and performance measurement across solutions, spanning generative and agentic AI, retrieval\-augmented systems, and traditional models including persistency, enrollment forecasting, service demand prediction, billing accuracy, and operational performance optimization.
- Oversee end\-to\-end Applied AI and data science lifecycle across portfolios, from problem framing through model development, validation, deployment, monitoring, and continuous improvement.
- Lead the identification and integration of new data sources, AI tooling, and quantitative methods into Employee Benefits and Operations workflows, improving service delivery, accuracy, scalability, and cost efficiency.
- Oversee domain\-level AI risk posture and model governance, ensuring alignment with Legal, Compliance, Model Risk, Privacy, Security, and Audit partners and maintaining readiness for regulatory review.
- Drive cross–line of business prioritization, investment planning, and workforce strategy, aligning initiatives to measurable business outcomes and capacity constraints.
- Champion reuse, standardization, and componentization of Applied AI and data science assets, ensuring alignment with enterprise AI platform strategy and enabling scalable deployment across portfolios.
- Partner with Employee Benefits, Operations, Technology, and Service leaders to embed Applied AI into employer onboarding, enrollment, billing, service, and operational strategies.
- Design and scale the Applied AI leadership system across supported lines of business, including succession pipelines, capability frameworks, and long\-term talent architecture.
- Define and lead a domain\-level Applied AI research and innovation agenda, balancing near\-term delivery with exploration of emerging techniques, tools, and capabilities.
- Monitor external AI, healthcare benefits, and regulatory trends (e.g., HIPAA, ERISA, data privacy), industry practices, and competitor capabilities to maintain competitive positioning and inform strategic direction.
Skills
- Demonstrated ability to lead Applied AI and data science strategy and execution across multiple lines of business or complex domains within a regulated enterprise environment.
- Proven experience leading leaders of leaders and scaling organizational capability across multiple teams, portfolios, and disciplines including Applied AI and data science.
- Strong technical and regulatory fluency across Applied AI, including generative and agentic AI, retrieval\-augmented systems, evaluation and monitoring frameworks, and production AI operations.
- Deep expertise in data science and quantitative methods, including forecasting, operational optimization, service analytics, demand modeling, persistency analysis, and cost/efficiency modeling.
- Applied understanding of unstructured data and retrieval approaches, as well as structured data modeling and feature engineering to support business decision\-making.
- Strong expertise in AI governance, model risk management, and responsible AI practices, with the ability to apply these consistently across both AI systems and traditional models.
- Demonstrated ability to drive business process transformation through the application of data science and Applied AI, including automation, optimization, and decision support.
- Ability to influence senior executives and enterprise forums through clear, data\-driven communication of technical trade\-offs, risks, and business impact.
- Experience driving cross\-LOB prioritization, investment decisions, and workforce planning aligned to measurable outcomes at scale.
- Strong business acumen with the ability to connect analytical outputs to Employee Benefits outcomes, including employer growth, enrollment, persistency, billing accuracy, service experience, and operational efficiency.
- Ability to balance long\-term strategic direction with near\-term execution and delivery effectiveness across a diverse portfolio of use cases.
- Strong judgment navigating regulatory, operational, and technical complexity across multiple domains and lines of business.
- Experience applying AI to service operations, including contact center intelligence, document processing, workflow automation, and customer experience optimization.
Education, Experience, Certifications and Licenses
- 20\+ years of applicable experience in Applied AI, data science, machine learning, or related quantitative fields.
- 10\+ years leading large, complex organizations, including leadership of senior leaders across multiple teams, portfolios, or domains.
- Demonstrated experience operating at VP level, influencing enterprise direction while owning outcomes across multiple lines of business or domains.
- Strong experience applying data science and AI within Employee Benefits, healthcare\-related domains, or complex service\-heavy insurance operations preferred.
- Bachelor’s degree required; Master’s or Ph.D. in a quantitative, technical, actuarial, or business field preferred and may offset experience.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short\-term or annual bonuses, long\-term incentives, and on\-the\-spot recognition. The annualized base pay range for this role is:
$225,600 \- $338,400
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
Salary Context
This $225K-$338K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 The Hartford, 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. This role's midpoint ($282K) sits 29% above the category median. Disclosed range: $225K to $338K.
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.
The Hartford AI Hiring
The Hartford has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US. Compensation range: $338K - $338K.
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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