AVP Applied AI, Small Business

The Hartford•Hartford, CT

1d•Hybrid

About The Position

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 Assistant Vice President (AVP), Applied AI leads data science, traditional machine learning, and agentic AI capabilities supporting The Hartford’s Business Insurance. This role partners closely with underwriting, product, actuarial, and technology leaders to deliver scalable, production ready models and ‑AI driven‑ decision systems that support complex risks, bespoke products, and profitable growth across specialty markets. This role will have a Hybrid work schedule, with the expectation of working in an office (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.
Proven ability to lead teams, building durable leadership capacity and consistent operating discipline across organizations.
Strong technical and regulatory fluency across applied AI, including generative and agentic AI, retrieval‑augmented systems, evaluation and monitoring practices, and production AI operations, sufficient to review, inform, and govern senior‑level decisions.
Applied understanding of unstructured data and retrieval approaches, including document ingestion pipelines, OCR, layout‑aware extraction, embeddings, hybrid and dense retrieval, reranking, metadata and lineage management, and PII controls.
Deep familiarity with AI governance, model risk management, responsible AI practices, and compliance‑by‑design expectations.
Demonstrated success translating strategy into coordinated execution and investment decisions across multiple teams over multi‑year horizons.
Ability to influence VPs and senior partners through clear, data‑driven communication of technical trade‑offs, evaluation outcomes, portfolio risks, and business impact.
12+ years of applicable experience with a Bachelor’s degree; fewer years may be accepted with a higher degree.
7–10+ years leading leaders, large portfolios, or complex programs.

Nice To Haves

Master’s or Ph.D. preferred in Machine Learning, Applied Mathematics, Data Science, Computer Science, or a similar analytical field, or progress towards a relevant professional designation.

Responsibilities

Own delivery, performance, and risk outcomes for one or more large, complex Applied AI portfolios spanning multiple teams, domains, or value streams.
Translate enterprise and Small Business AI priorities into multi‑year portfolio roadmaps and investment plans.
Ensure applied AI solutions deliver measurable business value while meeting standards for security, reliability, explainability, fairness, safety, and cost efficiency across solution types including generative and agentic AI, retrieval‑augmented systems, forecasting, recommendation systems, anomaly or fraud detection, and multimodal use cases.
Lead and develop high performing teams of AI scientists and engineers.
Build leadership bench strength through succession planning, coaching, and capability development.
Ensure consistent application of the Applied AI operating model, decision rights, delivery discipline, and escalation paths across the portfolio.
Reinforce shared expectations for quality, evaluation rigor, and production readiness.
Provide portfolio‑level technical direction and rigorous oversight, partnering closely with Principal ICs, Architecture, AI Platform, and Centers of Excellence.
Ensure consistent adoption of approved AI standards, patterns, and guardrails.
Review and thoughtfully evaluate portfolio‑level architectural choices, evaluation approaches, production readiness, and operational risk signals, guiding leaders through disciplined trade‑offs across quality, grounding, latency, cost, scalability, and regulatory risk.
Accountable for consistent application of evaluation and monitoring practices across the portfolio.
Ensure evaluation frameworks span classification, information retrieval, RAG/chat, forecasting, and customer or operational KPIs.
Oversee governance of metric taxonomies, thresholds, validation evidence, gold and synthetic test sets, A/B testing practices, drift detection, failure‑mode analysis, and incident response expectations.
Ensure evaluation results inform prioritization, release decisions, and risk management at the executive level.
Set portfolio‑level expectations and governance for unstructured data and retrieval practices, including document ingestion pipelines, parsing, OCR, layout‑aware extraction, metadata and lineage management, access controls, PII detection and redaction, and auditability.
Ensure retrieval strategy decisions, including embedding approaches, hybrid and dense retrieval patterns, reranking, grounding validation, and multilingual considerations, align with enterprise standards and regulatory requirements.
Accountable for portfolio-level AI governance ensuring alignment with Legal, Compliance, Model Risk, Privacy, Security, and Audit partners.
Maintain readiness for audits and regulatory review by ensuring governance artifacts, controls, escalation paths, and operational evidence are consistently established and enforced.
Escalate material risks, trade‑offs, and investment decisions to VPs with clear options and implications.
Partner with senior leaders across the Business, Technology, Transformation Office, Finance, and HR to align Applied AI delivery with business outcomes.
Influence portfolio funding, prioritization, and workforce planning through evidence‑based assessments of delivery performance, evaluation outcomes, and risk considerations.
Oversee portfolio‑level planning, dependencies, resourcing, and financial stewardship.
Adjust plans to address shifting priorities, capacity constraints, emerging technical risks, or regulatory changes.
Drive continuous improvement in delivery effectiveness, operational resilience, governance maturity, and value realization across the Applied AI portfolio.