Interested in this AI Product Manager role at Optum?
Apply Now →About This Role
Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.
We are seeking a Senior Manager, Technical Product Management to lead and scale product capabilities across our healthcare payer technology portfolio. This role sits at the intersection of business strategy, customer experience, data, and engineering, and is critical to translating customer and market insights into high impact, scalable products.
The ideal candidate is a trusted product leader who can drive product strategy and execution, influence across business and technology teams, and build solid product talent. This role requires deep experience managing complex product ecosystems, balancing competing priorities, and operating effectively in a regulated healthcare environment.
For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.
Primary Responsibilities:
- Product Strategy \& Customer Value
+ Partner with product managers, business stakeholders, and customers to capture Voice of Customer (VOC) and translate insights into product capabilities and jobs to be done
+ Drive product strategy through clear articulation of vision, value proposition, and strategic and feature roadmaps
+ Create and communicate compelling value propositions through internal marketing and stakeholder communications
+ Ensure all features and initiatives are clearly tied to OKRs and business outcomes
+ Manage complex requirements and continuously refine products based on evolving business goals, market needs, and customer feedback
- Product Execution \& Delivery
+ Oversee prioritization and trade off decisions, balancing customer experience, business requirements, technical constraints, performance, and operational needs
+ Decompose high level capabilities into features, epics, and user stories that are clear, actionable, and measurable
+ Partner closely with engineering teams to define requirements, user stories, and acceptance criteria
+ Support product backlog management and prioritization across scrum and feature teams
+ Drive feature level budgets and ensure efficient use of resources
+ Support integration testing, automation practices, and DevOps aligned delivery models
- Technical \& Domain Expertise
+ Maintain a solid understanding of end to end solutions, including system integrations and data flows
+ Understand feature impacts and dependencies across platforms and portfolios
+ Actively advance understanding of the product operating model and product culture across teams
+ Demonstrate working knowledge of modern product and delivery tools such as Aha, Rally, GitHub, and DevOps practices
+ Effectively balance the needs of operations, data, and engineering teams
- Stakeholder Leadership \& Influence
+ Serve as a credible and trusted partner to senior business and technology leaders
+ Speak fluently to both technical and non technical audiences across teams and levels
+ Lead complex, cross functional discussions and drive alignment through fact based, customer centric decision making
+ Diffuse conflict and drive consensus by focusing on the greater good of the product and customer needs
+ Anticipate stakeholder needs by preparing options, trade offs, and recommendations in advance
- People Leadership \& Talent Development
+ Provide situational coaching and support to other product managers and product teams
+ Positively influence product outcomes through mentorship and collaboration
+ Actively attract, assess, and hire top product talent using a calibrated bar for excellence
+ Contribute to hiring decisions and leverage professional networks to identify qualified candidates
+ Train and mentor junior interviewers through shadowing and best practice guidance
+ Foster a culture of transparency, growth mindset, and continuous learning
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:
- Bachelor's Degree
- 10\+ years of experience in Product Management, with significant experience in technical or platform products
- Experience as a Product Manager supporting Data Science, Software applications, and / or AI projects
- Solid experience working with engineering, data, and architecture teams
- Experience with Agile delivery models and product management tooling (Aha, Rally, GitHub, etc.)
- Demonstrated ability to manage complex product portfolios and cross team dependencies
- Proven success driving product strategy, roadmaps, and execution at scale
Preferred Qualifications:
- Experience in US Healthcare Payer domain (claims, eligibility, care management, provider data, analytics, etc.)
- Experience scaling product organizations or leading through transformation
- Exposure to data platforms, analytics products, or AI/ML enabled solutions
- Familiarity with regulated environments and compliance driven product delivery
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far\-reaching choice of benefits and incentives. The salary for this role will range from $112,700 to $193,200 annually based on full\-time employment. We comply with all minimum wage laws as applicable.
*At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone\-of every race, gender, sexuality, age, location and income\-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes \- an enterprise priority reflected in our mission.*
*UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.*
*UnitedHealth Group is a drug\-free workplace. Candidates are required to pass a drug test before beginning employment.*
Salary Context
This $112K-$193K range is in the lower quartile for AI Product Manager roles in our dataset (median: $189K across 161 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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Optum, 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 in Demand for This Role
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 $213,800 based on 583 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $112K to $193K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Optum AI Hiring
Optum has 21 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Engineering Manager, AI Software Engineer. Positions span Eden Prairie, MN, US, Minnetonka, MN, US, Basking Ridge, NJ, US. Compensation range: $107K - $343K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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