Product Owner - Advanced Analytics & Research
Vision Creation & Product Strategy
Define and communicate the AI Innovation Lab product vision, outcomes, and success metrics; maintain a roadmap that balances innovation with enterprise readiness.
Create decision frameworks for adopt / adapt / defer / reject outcomes 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.
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.
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).
Research Delivery & Transition to Production
Oversee rapid prototypes and proofs-of-concepts from concept through stakeholder validation; ensure learnings are documented (wins and failures).
Capture learnings and document them for future reference.
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.
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 regression control as systems evolve.
Champion MLOps/LLMOps practices: reproducibility, versioning (models/prompts), CI/CD patterns, monitoring, and controlled rollout strategies.
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 oversight appropriate for insurance.
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.
Team Management & People Leadership
The Product Owner also leads the Data Science team, ensuring clear goals, effective prioritization, and high-quality delivery.
They are responsible for performance management, talent development, and supporting HR-related activities that foster a healthy, collaborative team culture.
This includes guiding career growth, facilitating feedback cycles, and aligning team capabilities with evolving business needs.
Change Leadership & Culture
Act as a visible change leader—guiding adoption of AI-enabled workflows, building trust through transparency, and ensuring responsible use.
Mentor team members and contribute to a culture of continuous learning and high-quality delivery.