AI Product Manager

Lincoln Financial•Radnor, PA

5d•$96,900 - $176,200•Hybrid

About The Position

Lincoln Financial Group is seeking a detail-oriented and delivery-focused Product Owner to join our AI Product & Delivery organization. In this role, leading innovation squads, you will own the day-to-day execution of AI product features and capabilities within an assigned product domain, working hands-on with data science, engineering, and business domain teams to bring AI solutions from backlog to production. Reporting to the VP/AVP of AI Products, you will serve as the connective tissue between business process reimagination and technical delivery — writing clear user stories, managing sprint-level priorities, supporting model evaluations, and ensuring AI features meet quality, compliance, and user experience standards. This role is ideal for a practitioner who thrives in the details and is eager to grow their AI product career in a regulated financial services environment.

Requirements

3–10 years of experience in product management, product ownership, or a closely related role.
Hands-on experience working on AI, ML, or data-driven products — including direct collaboration with data science or engineering teams.
Familiarity with LLM concepts such as prompt engineering, model evaluation, and output quality assessment.
Experience with agile methodologies and backlog management tools (e.g., Jira, Linear, Productboard).
Strong written communication skills — able to write clear, unambiguous user stories, PRDs, and feature specifications.
Bachelor's degree in Computer Science, Data Science, Business, or a related field.

Nice To Haves

Hands-on experience with LLM APIs or enterprise AI platforms (e.g., Azure OpenAI, AWS Bedrock, Anthropic Claude, Google Vertex AI).
Exposure to ML evaluation frameworks, annotation workflows, or AI observability tools (e.g., LangSmith, Weights & Biases).
Familiarity with responsible AI practices including bias detection, fairness evaluation, and model explainability.
Domain experience in life insurance, annuities, retirement planning, or employee benefits.
Product or AI-related certifications (Pragmatic, AIPMM, data science, or ML-related).

Responsibilities

Own and maintain the product backlog for assigned AI features or squads, ensuring stories are well-defined, prioritized, and ready for sprint execution.
Write detailed user stories, acceptance criteria, and feature specifications for AI-driven capabilities in collaboration with engineering and data science teams.
Lead actively in agile ceremonies — sprint planning, standups, reviews, and retrospectives — keeping delivery on track and surfacing blockers early.
Coordinate UAT, QA, and launch readiness activities for AI feature releases, ensuring quality and compliance standards are met before go-live.
Support the AVP in managing timelines, dependencies, and risks across feature workstreams.
Serve as the day-to-day product contact for business SMEs, engineering squads, and UX designers within assigned AI feature areas.
Communicate feature status, trade-offs, and delivery risks clearly to the AVP and relevant business partners.
Facilitate working sessions and backlog refinement meetings to drive team alignment and shared understanding of requirements.
Support change management by helping business partners understand, test, and adopt new AI-driven capabilities.
Apply responsible AI principles — fairness, transparency, explainability, and privacy — in the definition and acceptance of AI features.
Escalate potential compliance, bias, or safety concerns identified during model evaluation or feature review to the AVP and relevant risk partners.
Ensure AI feature documentation, testing evidence, and release artifacts meet Lincoln's internal governance and audit standards.
Collaborate with Trust & Safety, Legal, and Compliance teams as needed to support feature-level risk reviews.
Define and track feature-level success metrics including adoption, task completion, model accuracy, and user satisfaction.
Analyze usage data, model performance logs, and user feedback to identify improvement opportunities and inform backlog prioritization.
Contribute to sprint-level reporting and help maintain product dashboards that surface key delivery and quality metrics.
Share learnings from evals, user testing, and post-launch monitoring with the broader AI Product & Delivery team.
Partner with data scientists and ML engineers to understand model capabilities, limitations, and evaluation results for assigned AI features.
Assist in designing and executing evaluations (evals) for LLM-powered features — assessing output quality, accuracy, relevance, and safety against defined benchmarks.
Review and annotate model outputs as part of human-in-the-loop feedback processes, identifying failure modes, edge cases, and opportunities for improvement.
Support the creation of eval datasets, test case libraries, and regression frameworks to enable consistent model performance tracking.
Assist in prompt testing and iteration — running structured experiments to understand how prompt changes affect model behavior and output quality.
Help monitor model performance post-launch and flag regressions or unexpected behavior to the data science team.