AVP Applied AI, Claims

The Hartford•Hartford, CT

4d•Hybrid

About The Position

The Hartford is hiring an AVP leading Applied AI for the Claims organization. This leader will play a pivotal role in transforming the end-to-end Claims process by developing and embedding AI capabilities to enhance outcomes, process and experience. This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week. Candidates must be eligible to work in the US without company sponsorship.

Requirements

Demonstrated experience leading large, complex Applied AI portfolios in regulated enterprise environments with consistent delivery discipline and risk management.
Strong business partnership and influence skills—able to translate Claims objectives into AI product strategy/roadmaps and drive adoption through operating model alignment and stakeholder engagement.
Deep technical fluency across Predictive ML + Generative + Agentic AI, including prompt engineering, RAG, agentic frameworks, evaluation methods, guardrail management, and AI/MLOps/model lifecycle management.
Strong governance and responsible AI grounding: model risk management, fairness/safety, explainability, monitoring, and compliance-by-design.
Applied understanding of unstructured data and retrieval systems (document ingestion pipelines, OCR, layout-aware extraction, embeddings, hybrid/dense retrieval, reranking, metadata/lineage, PII controls).
Proven ability to build and grow high-performing technical teams and establish operating rhythms across multiple teams/value streams.
Exceptional communication skills—able to communicate effectively at all levels and convert complex technical trade-offs into clear business implications and decisions.
Strong strategic thinking, analytical capability, negotiation/influence, conflict resolution, and ability to work autonomously across multiple priorities.
12+ years of applicable experience in Data Science, Applied AI, Analytics, Machine Learning Engineering, or related fields, including building and scaling AI solutions in production.
7+ years of formal people leadership experience (leading leaders and/or multi-team organizations.
Bachelor’s degree required
Proficiency with cloud platforms (preferably AWS or GCP) and their AI/ML ecosystems.

Nice To Haves

Insurance and/or Claims domain experience strongly preferred, with demonstrated success driving AI-enabled process transformation.
Master’s or Ph.D. preferred in Machine Learning, Data Science, Computer Science, Applied Mathematics, or similar analytical field.

Responsibilities

Own delivery, performance, and risk outcomes for a large, complex Claims Applied AI portfolio spanning multiple teams, domains, and value streams; translate Claims and enterprise AI priorities into a multi-year roadmap and investment plan.
Drive measurable business value across the end-to-end claims journey by developing, testing, deploying, and scaling Predictive, Generative, and Agentic AI solutions (e.g., forecasting, triage, recommendations, anomaly/fraud detection, RAG/assistants, and agentic workflow orchestration).
Be the senior business partner for Claims leaders: proactively understand short- and long-term goals, shape the problem statements, define success measures, and ensure solutions are adopted and embedded into core claim processes and colleague experiences.
Build deep partnerships within portfolio and value stream frameworks; promote agile, iterative delivery through cross-functional teams to ensure fit-for-purpose solutions and rapid learning cycles.
Lead and develop senior leaders and teams (e.g., asset owners, data engineers, data scientists, ML engineers), building bench strength through succession planning, coaching, and capability development while creating an engaged and inclusive culture
Provide portfolio-level technical direction and oversight, partnering with Principal Individual Contributors, Architecture, AI Platform, and Centers of Excellence to drive consistent adoption of approved standards, patterns, and guardrails
Ensure disciplined architecture and delivery trade-offs across quality, grounding, latency, cost, scalability, and regulatory risk—especially for GenAI and agentic solutions operating in claims environments.
Establish and enforce evaluation, monitoring, and production readiness across solution types (classification, regression, retrieval/RAG/chat, forecasting), including metric taxonomies, thresholds, validation evidence, gold/synthetic test sets, A/B testing, drift detection, failure mode analysis, and incident response expectations.
Set governance expectations for unstructured data and retrieval across Claims (document ingestion, parsing/OCR, layout-aware extraction, metadata/lineage, access controls, PII detection/redaction, auditability), including embedding/retrieval strategies and grounding validation aligned to enterprise standards.
Accountable for AI governance and compliance-by-design across the Claims portfolio, partnering with Legal, Compliance, Model Risk, Privacy, Security, and Audit; maintain audit readiness with clear controls, artifacts, escalation paths, and operational evidence.
Influence technology integration and platform strategy by partnering with Technology, Data, AI Platform, AI/MLOps, and Architecture teams on tooling, standard work, reusable capabilities, and scalable patterns for Predictive, Generative, and Agentic AI.
Champion reuse and scalability by partnering with AI platform owners and peers to develop and integrate reusable Claims AI capabilities within The Hartford’s AI platform.
Provide thought leadership and change leadership: educate stakeholders, identify new AI opportunities, advance a data-driven culture, and drive change to core Claims processes through innovative quantitative/AI techniques.
Oversee portfolio planning, dependencies, resourcing, and financial stewardship, adapting to changing priorities, capacity constraints, technical risks, and regulatory needs while driving continuous improvement in delivery effectiveness and value realization.
Maintain strong knowledge of business processes and data sources and stay current on advancements in Machine Learning, GenAI/agentic frameworks, evaluation/guardrails, MLOps, cloud engineering, and emerging technologies.