Key Responsibilities

1) Vision Creation & Product Strategy

• Define and communicate the AI Innovation Lab product vision, outcomes, and success metrics;maintaina roadmap that balances innovation with enterprise readiness. 

• Create decision frameworks foradopt/ adapt/ defer / rejectoutcomes so the Lab’s learning directly informs enterprise AI strategy. 

• Own prioritization of initiatives across multiple business requests based on value, feasibility, risk, and operational constraints. 

2) Customer Engagement (Business Stakeholders) & Executive Communication 

• Serve as the primary Lab-facing leader for business stakeholders and executives: intake, discovery, expectation-setting, and ongoing engagement.  

• Translate ambiguous business asks into clear problem statements, hypotheses, and acceptance criteria for research and prototypes. 

• Deliver clear, credible readouts—able to explain tradeoffs, risks, and readiness to both technical and non-technical audiences. 

3) Product Design & Research Planning

• Drive product discovery: user journeys, workflow design, guardrails, human-in-the-loop controls, and measurable definitions of value. 

• Partner with technical team members to ensure prototypes align with enterprise constraints and API-first integration principles. 

• Ensure evaluation plans exist before building (quality measures, go/no-go criteria, and what “good” looks like). 

4) Research Delivery & Transition to Production

• Oversee rapid prototypes andproofs-of-conceptfrom concept through stakeholder validation; ensure learnings are documented (wins and failures). 

• Coordinate with Enterprise Architecture and IT delivery teams to shape handoffs that can succeed in production (security, operations, integration, support model). 

• Ensure the Lab produces “decision-grade” outputs: feasibility, limitations, risk, and a recommended path forward. 

5) Full-Stack AI Lifecycle Ownership & Optimization

• Demonstrated understanding of the full AI product lifecycle: problem framing → data readiness → model/approach selection (ML vs GenAI) → prototyping → evaluation → governance/security → production transition → monitoring and continuous improvement. 

• Drive optimization across performance, cost, and reliability: latency/throughput, retrieval quality (RAG), prompt/agent instruction tuning, and regressioncontrol assystems evolve. 

• ChampionMLOps/LLMOpspractices: reproducibility, versioning (models/prompts), CI/CD patterns, monitoring, and controlled rollout strategies. 

6) AI Agents & Customer-Facing AI Applications

• Demonstrated experience creating AI agents (single and multi-agent) that use tools/APIs to execute workflows with guardrails and human oversightappropriate forinsurance. 

• Experience building customer-facing AI applications (internal customers such as underwriting, claims, actuarial, and analytics teams), including conversational UX patterns, RAG grounding, structured outputs, and feedback loops that build user trust. 

• Define and drive production readiness for agent solutions (failure modes/fallbacks, monitoring, operational handoff expectations) in partnership with Enterprise Architecture and delivery teams. 

7)Team Management & People Leadership

• The Product Owner also leads the Data Science team, ensuring clear goals, effective prioritization, andhighqualitydelivery. They are responsible forperformance management, talent development, and supporting HRrelatedactivities that foster a healthy, collaborative team culture. This includes guiding career growth, facilitatingfeedback cycles, and aligning team capabilities with evolving business needs.

8)Change Leadership & Culture

• Act as a visible change leader—guiding adoption of AI-enabled workflows, building trust through transparency, and ensuring responsible use. 

• Mentor teammembers andcontribute to a culture of continuous learning and high-quality delivery. 

Required Qualifications

• Proven senior leadership in product ownership/product management/innovation leadership delivering outcomes in ambiguous environments (especially where technology feasibility must be proven). 

• Exceptional written and verbal communication skills—able to align executives, business stakeholders, and technical teams; strong storytelling with evidence. 

• Strong understanding of enterprise integration principles, including API-first thinking and how AI capabilities transition into production systems. 

• Strong understanding of AI/ML and GenAI solution patterns sufficient to lead discovery and evaluate approaches objectively (including ML evaluation fundamentals and LLM/RAG patterns). 

• Demonstrated experience creating AI agents and customer-facing AI applications withappropriate guardrailsand human-in-the-loop controls. 

• Demonstrated understanding ofMLOps/LLMOps, evaluation rigor, monitoring, and optimization across performance, cost, and reliability. 

Preferred Qualifications

• Experience in insurance, financial services, or other regulated industries. 

• Hands-on familiarity with agentic AI frameworks and orchestration patterns such asLangChain/ LangGraph,CrewAI, or similar. 

• Familiarity with enterprise AI interoperability patterns and standards such as Model Context Protocol (MCP), tool registries, and A2A (agent-to-agent) coordination concepts for enterprise workflows. 

• Experience with Microsoft Azure cloud services. 

• Background working with actuarial, underwriting, or claims processes and/or experience transitioning prototypes to production teams.