Qualifications
Responsibilities
Benefits
Great American’s culture is built on connection, shared learning, and strong relationships. To support this, employees in this role are expected to be on-site a minimum of two days a week if local to Cincinnati, with the potential to work three days remotely. Core in‑office days are Tuesday and Thursday but will be determined by business needs.
As the insurance industry undergoes digital transformation, the AI Innovation Lab serves as Great American’s proving ground for emerging AI capabilities. Team members evaluate, prototype, andvalidateAI technologies against real businessneeds, determiningwhat’s ready for enterprise adoption and what isn’t. This is applied research with a purpose: every initiative ties directly to business requests, and successful proofs-of-concept are handed off to IT delivery teams for production implementation.
What Makes This Role Unique
Thisisn’ta traditional research position, and it isn’ta traditional development role. It’ssomething in between—now with senior product ownership and change leadership:
Vision-to-value ownership: You create and evolve the Lab’s product vision androadmapso work stays tightly aligned to enterprise priorities and measurable outcomes.
Rapid experimentation:You’llgo deep on a technology, guide the team to build working prototypes,determinefit-for-purpose, and move to the next challenge.
Business-driven focus: Every project originates froma real businessask—supporting underwriters, actuaries, claims professionals, and analysts across the enterprise.
Fail-fast culture: A well-documented “no, and here’s why” is as valuable as a successful proof-of-concept.
Partnership model to production: You work with Enterprise Architecture and IT deliveryteamsso innovations can be operationalized—not just demoed.
Human-in-the-loop philosophy: Ethical, transparent, explainable AI is foundational in insurance; you ensure designs reflect that from day one.
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.
Business Unit:
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