Senior AI Product ManagerLocation: EST time zone, Remote/Hybrid in NYClient: Cigna (Keep confidential)Experience: minimum 12+ years required with major experience into healthcare and consultingConsulting companies to search: Deloitte, EY, Epam, Accenture, KPMG, McKinsey & Company, Boston Consulting Group (BCG), Bain & Company, (PwC), Oliver Wyman, Milliman, IQVIAShort JD HighlightsHealthcare - Preferably Payer/PBM not clinical/pharma (possibly acceptable to have highly regulated experience industry experience if the AI Product side is very strong)AI Product Mgt - Has demonstrated experience of having built AI Products and taken from Ideation to ProductionStakeholder and Business EngagementExecution in Agile/FDE environment.Detailed JDRole SummaryWe 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 ResponsibilitiesIdentify & Prioritize High-Impact AI Use CasesWork with business, operations, and clinical stakeholders to identify opportunities across:Benefits verification (BV) / Prior Authorization (PA)Patient onboarding & educationAdherence & refill managementCall center / pharmacist workflowsTranslate business pain points into clear problem statements and opportunity areasDrive Rapid ExperimentationConvert ideas into testable hypotheses and experimentsDesign MVPs and pilots using:AI/LLM capabilitiesWorkflow automationData-driven decisioningRun fast iteration cycles:Prototype ? test ? learn ?
refineDefine success metrics and evaluate outcomes quicklyBuild & Deliver AI-Enabled ProductsOwn end-to-end product lifecycle:Problem definition ? solution design ? development ?
launch ? iterationPartner with Engineering, Data Science, and Design teams to:Build scalable, production-ready solutionsIntegrate AI into real workflows (not just standalone models)Ensure usability for frontline users (pharmacists, agents, care teams)Bridge Business, Clinical, and Technology TeamsAct as a translator between:Business/operations (patient services)Technology (engineering, AI/ML teams)Ensure solutions are:Operationally feasibleClinically appropriateTechnically scalableEmbed AI into Real WorkflowsDesign solutions that:Augment human decision-making (not replace blindly)Incorporate human-in-the-loop controlsFit seamlessly into existing systems (CRM, workflow tools, call center platforms)Drive adoption across end usersMeasure Impact & ScaleTrack: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 deploymentRequired Qualifications6–10+ years in Product Management or Product OwnershipStrong healthcare domain experience, preferably in:Patient services / HUB servicesPayer or PBM ecosystemsProven experience working on data-driven or AI-enabled productsDemonstrated ability to drive rapid experimentation and MVP deliveryStrong ability to work across clinical, and technical teamsPreferred QualificationsExperience with:Prior Authorization (PA), Benefits Verification (BV), or patient onboarding workflowsCall center / omnichannel patient engagement platformsFamiliarity with:LLMs / Generative AI applicationsWorkflow automation toolsData platforms (e.g., modern data stack)Experience in regulated environments (HIPAA, compliance constraints)Key TraitsHighly execution-oriented (bias for action)Strong problem framer (not just backlog manager)Comfortable with ambiguity and iterationDeep empathy for patients and frontline usersData-driven decision makerAble to balance speed vs compliance vs scaleSuccess Profile (6 Months)Delivered 2–3 AI-driven pilots with measurable business impactEstablished repeatable experimentation approach within the teamDemonstrated improvements in:Cycle time for new features/use casesOperational efficiency