VP, Applied AI & Data Science for Employee Benefits

The Hartford

Chicago (IL)

Hybrid

USD 225,600 - 338,400

Full time

14 days+

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Job summary

The Hartford is seeking a Vice President, Applied AI – Employee Benefits & Operations to define and lead the next generation of AI-powered transformation across the business. You will oversee a portfolio of ML, Generative AI, and predictive analytics solutions while shaping enterprise AI strategy and governance across enrollment, billing, customer service, and operations.

This role can be Hybrid or Remote, with in-office days for those near Hartford, CT; Chicago, IL; Columbus, OH; or Charlotte,

Qualifications

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.

Responsibilities

Own Applied AI strategy across Employee Benefits and Operations, including enrollment, billing, service delivery, and operations.
Define domain-level Applied AI strategy and influence enterprise AI direction through evidence-based recommendations.
Lead executive decision-making across supported lines of business, balancing quality, risk, cost, scalability, and value.
Drive transformation of processes through Applied AI and data science, including enrollment optimization, service automation, and billing accuracy.
Oversee enterprise AI operating model and governance across portfolios with defined decision rights.
Ensure adherence to AI governance frameworks, model risk management, and responsible AI practices.
Set direction for evaluation and performance across generative and agentic AI, retrieval-augmented systems, and traditional models.
Oversee end-to-end Applied AI and data science lifecycle from problem framing to deployment and improvement.
Lead integration of new data sources, AI tooling, and quantitative methods into workflows to improve delivery and efficiency.
Drive AI risk posture and governance with Legal, Compliance, Model Risk, Privacy, Security, and Audit partners.