Senior AI Product Manager

Position- Senior AI Product Manager

Location- EST time zone, Remote/Hybrid in NY

Client- Cigna (Keep confidential)

Experience- minimum 12+ years required with major experience into healthcare and consulting

Consulting Companies to search- Deloitte, EY, Epam, Accenture, KPMG, McKinsey & Company, Boston Consulting Group (BCG), Bain & Company, (PwC), Oliver Wyman, Milliman, IQVIA

Short JD Highlights-

1. Healthcare - Preferably Payer/PBM not clinical/pharma (possibly acceptable to have highly regulated experience industry experience if the AI Product side is very strong)
2. AI Product Mgt - Has demonstrated experience of having built AI Products and taken from Ideation to Production
3. Stakeholder and Business Engagement
4. Execution in Agile/FDE environment.

Detailed JD-

Role Summary

We are seeking an AI Product Manager with deep healthcare domain experience to drive rapid experimentation and delivery of AI-enabled products.

This role will work at the intersection of business, workflows, operations, and technology to identify high-impact opportunities, design solutions, and rapidly test and scale AI-driven capabilities across pharmacy and patient service journeys.

This is a hands-on, execution-oriented role focused on turning ideas into measurable outcomes quickly.

Key Responsibilities

1. Identify & Prioritize High-Impact AI Use Cases

• Work with business, operations, and clinical stakeholders to identify opportunities across:
  • Benefits verification (BV) / Prior Authorization (PA)
  • Patient onboarding & education
  • Adherence & refill management
  • Call center / pharmacist workflows
• Translate business pain points into clear problem statements and opportunity areas

2. Drive Rapid Experimentation

• Convert ideas into testable hypotheses and experiments
• Design MVPs and pilots using:
  • AI/LLM capabilities
  • Workflow automation
  • Data-driven decisioning
• Run fast iteration cycles:
  • Prototype test learn refine
• Define success metrics and evaluate outcomes quickly

3. Build & Deliver AI-Enabled Products

• Own end-to-end product lifecycle:
  • Problem definition solution design development launch iteration
• Partner with Engineering, Data Science, and Design teams to:
  • Build scalable, production-ready solutions
  • Integrate AI into real workflows (not just standalone models)
• Ensure usability for frontline users (pharmacists, agents, care teams)

4. Bridge Business, Clinical, and Technology Teams

• Act as a translator between:
  • Business/operations (patient services)
  • Technology (engineering, AI/ML teams)
• Ensure solutions are:
  • Operationally feasible
  • Clinically appropriate
  • Technically scalable

5. Embed AI into Real Workflows

• Design solutions that:
  • Augment human decision-making (not replace blindly)
  • Incorporate human-in-the-loop controls
  • Fit seamlessly into existing systems (CRM, workflow tools, call center platforms)
• Drive adoption across end users

6. Measure Impact & Scale

• Track:
  • Operational efficiency (e.g., reduced handling time, faster approvals)
  • Experience metrics (patient, agent, pharmacist)
  • Business outcomes (conversion, adherence, cost reduction)
• Scale successful pilots into broader deployment

Required Qualifications

• 6 10+ years in Product Management or Product Ownership
• Strong healthcare domain experience, preferably in:
  • Patient services / HUB services
  • Payer or PBM ecosystems
• Proven experience working on data-driven or AI-enabled products
• Demonstrated ability to drive rapid experimentation and MVP delivery
• Strong ability to work across clinical, and technical teams

Preferred Qualifications

• Experience with:
  • Prior Authorization (PA), Benefits Verification (BV), or patient onboarding workflows
  • Call center / omnichannel patient engagement platforms
• Familiarity with:
  • LLMs / Generative AI applications
  • Workflow automation tools
  • Data platforms (e.g., modern data stack)
• Experience in regulated environments (HIPAA, compliance constraints)

Key Traits

• Highly execution-oriented (bias for action)
• Strong problem framer (not just backlog manager)
• Comfortable with ambiguity and iteration
• Deep empathy for patients and frontline users
• Data-driven decision maker
• Able to balance speed vs compliance vs scale

Success Profile (6 Months)

• Delivered 2 3 AI-driven pilots with measurable business impact
• Established repeatable experimentation approach within the team
• Demonstrated improvements in:
  • Cycle time for new features/use cases
  • Operational efficiency (e.g., reduced manual effort)
  • User adoption (agents/pharmacists/patients)
• Built strong alignment across business and tech stakeholders