Job description

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 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 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 driving trade-offs across quality, risk, cost, scalability, and time-to-value for Applied AI and data science initiatives. Drive transformation of Employee Benefits 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 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, Technology, and Service leaders to embed Applied AI into EB 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 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 About Us | Our Culture | What It’s Like to Work Here | Perks & Benefits

Requirements

Must-haves

VP Data Science - GD04AE
Primary Job Responsibilities
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 driving trade-offs across quality, risk, cost, scalability, and time-to-value for Applied AI and data science initiatives.
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 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, Technology, and Service leaders to embed Applied AI into EB 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.
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.

Nice-to-haves

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.

Application prep

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Keywords to mirror

aimachine learning

Platform: workday
Effort: long