Overview In this Senior Product Manager role, you define and execute the vision for Optum Payment Integrity's Intelligent Fraud Detection product, from pilot to scale. You'll shape strategy, packaging, and go-to-market to deliver measurable client value and market differentiation. You collaborate with data science, engineering, and GTM teams to build AI-powered fraud detection capabilities at scale. This remote-friendly position combines data, technology, and healthcare domain expertise to improve cost efficiency and outcomes.
Compensation / Benefits- comprehensive benefits package
- incentive and recognition programs
- equity stock purchase
- 401k contribution
Responsibilities- Define the market vision and multi-year product strategy
- Translate market needs and competitive intel into actionable product direction
- Align investments to deliver client value, revenue growth, and differentiation
- Collaborate with portfolio PMs to support scalability and client success
- Shape strategy for using data science, AI, and network analytics for fraud detection
- Translate strategy into roadmaps, requirements, and prioritized outcomes
- Work with engineering, AI/ML, analytics, and operations to deliver data-driven solutions
- Prioritize to balance innovation, debt reduction, and scalability
- Ensure timely, high-quality product launches aligned to GTM goals
- Define KPIs and monitor adoption, ROI, and client outcomes
- Partner with GTM, sales, and marketing to drive adoption and expansion
- Stay updated on market trends to update roadmaps
Key requirements- 7+ years in product management or strategy
- 5+ years in fraud and abuse within payment integrity
- 3+ years with data-intensive products and data science teams
- Experience in healthcare, preferably payment integrity
- Experience launching/scaling 0-1 products in cloud/AI platforms
- Strong understanding of analytics, network analysis, anomaly detection, or ML for fraud
- End-to-end ownership of complex healthcare products improving fraud detection
- Ability to work with data science teams to define objectives and translate findings into product features
- Collaborative with engineering, AI/ML, and data science teams
- strategic mindset
- cross-functional collaboration
- stakeholder communication at executive level
- AI/ML integration
- data-intensive product design
- network analytics
