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About This Role
Date: Aug 3, 2026
Primary Location: Radnor, PA, US
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Company: Lincoln Financial
Alternate Locations: Radnor, PA (Pennsylvania); Greensboro, NC (North Carolina)
Work Arrangement:
Hybrid : Employee will work 3 days a week in a Lincoln office
Relocation assistance: is not available for this opportunity.
Requisition \#: 76401
The Role at a Glance
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.
What you'll be doing
AI Feature Ownership \& Delivery
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.
Stakeholder Engagement
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.
Responsible AI \& Compliance
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.
Performance \& Insights
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.
Data Science \& Model Evaluation Support
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.
What we’re looking for
Must\-have (required)
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\-have (preferred):
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).
Application Deadline
Applications for this position will be accepted through August 31, 2026, subject to earlier closure due to applicant volume.
What’s it like to work here?
At Lincoln Financial, we love what we do. We make meaningful contributions each and every day to empower our customers to take charge of their lives. Working alongside dedicated and talented colleagues, we build fulfilling careers and stronger communities through a company that values our unique perspectives, insights and contributions and invests in programs that empower each of us to take charge of our own future.
What’s in it for you:
Clearly defined career tracks and job levels, along with associated behaviors for each of Lincoln's core values and leadership attributes
Leadership development and virtual training opportunities
PTO/parental leave
Competitive 401K and employee benefits
Free financial counseling, health coaching and employee assistance program
Tuition assistance program
Work arrangements that work for you
Effective productivity/technology tools and training
The pay range for this position is $96,900 \- $176,200 with anticipated pay for new hires between the minimum and midpoint of the range and could vary above and below the listed range as permitted by applicable law. Pay is based on non\-discriminatory factors including but not limited to work experience, education, location, licensure requirements, proficiency and qualifications required for the role. The base pay is just one component of Lincoln’s total rewards package for employees. In addition, the role may be eligible for the Annual Incentive Program, which is discretionary and based on the performance of the company, business unit and individual. Other rewards may include long\-term incentives, sales incentives and Lincoln’s standard benefits package.
About The Company
Lincoln Financial (NYSE: LNC) helps people to confidently plan for their version of a successful future. We focus on identifying a clear path to financial security, with products including annuities, life insurance, group protection, and retirement plan services.
With our 120\-year track record of expertise and integrity, millions of customers trust our solutions and service to help put their goals in reach.
Lincoln Financial Distributors, a broker\-dealer, is the wholesale distribution organization of Lincoln Financial. Lincoln Financial is the marketing name for Lincoln Financial Corporation and its affiliates including The Lincoln National Life Insurance Company, Fort Wayne, IN, and Lincoln Life \& Annuity Company of New York, Syracuse, NY. Lincoln Financial affiliates, their distributors, and their respective employees, representatives and/or insurance agents do not provide tax, accounting or legal advice.
Lincoln is committed to creating an inclusive environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
Follow us on Facebook, X, LinkedIn, Instagram, and YouTube. For the latest company news, visit our newsroom.
Be Aware of Fraudulent Recruiting Activities
If you are interested in a career at Lincoln, we encourage you to review our current openings and apply on our website. Lincoln values the privacy and security of every applicant and urges all applicants to diligently protect their sensitive personal information from scams targeting job seekers. These scams can take many forms including fake employment applications, bogus interviews and falsified offer letters.
Lincoln will not ask applicants to provide their social security numbers, date of birth, bank account information or other sensitive information in job applications. Additionally, our recruiters do not communicate with applicants through free e\-mail accounts (Gmail, Yahoo, Hotmail) or conduct interviews utilizing video chat rooms. We will never ask applicants to provide payment during the hiring process or extend an offer without conducting a phone, live video or in\-person interview. Please contact Lincoln's fraud team at [email protected] if you encounter a recruiter or see a job opportunity that seems suspicious.
Additional Information
This position may be subject to Lincoln’s Political Contribution Policy. An offer of employment may be contingent upon disclosing to Lincoln the details of certain political contributions. Lincoln may decline to extend an offer or terminate employment for this role if it determines political contributions made could have an adverse impact on Lincoln’s current or future business interests, misrepresentations were made, or for failure to fully disclose applicable political contributions and or fundraising activities.
Any unsolicited resumes or candidate profiles submitted through our web site or to personal e\-mail accounts of employees of Lincoln Financial are considered property of Lincoln Financial and are not subject to payment of agency fees.
Lincoln Financial ("Lincoln" or "the Company") is an Equal Opportunity employer and, as such, is committed in policy and practice to recruit, hire, compensate, train and promote, in all job classifications, without regard to race, color, religion, sex, age, national origin or disability. Opportunities throughout Lincoln are available to employees and applicants are evaluated on the basis of job qualifications. If you are a person with a disability that impedes your ability to express your interest for a position through our online application process, or require TTY/TDD assistance, contact us by calling (866\) 922\-6543\.
Salary Context
This $96K-$176K range is in the lower quartile for AI Product Manager roles in our dataset (median: $185K across 167 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Lincoln Financial, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($136K) sits 37% below the category median. Disclosed range: $96K to $176K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Lincoln Financial AI Hiring
Lincoln Financial has 3 open AI roles right now. They're hiring across Data Scientist, AI Product Manager, AI/ML Engineer. Based in Radnor, PA, US. Compensation range: $176K - $176K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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