AI Product Manager - remote opportunity

Tivity Health is a leading provider of healthy life-changing solutions, and they are seeking a strategic AI Product Manager to build an AI Center of Excellence. This role involves defining the vision and strategy for AI, shipping production-ready AI products, and driving AI adoption across the organization.

Responsibilities:

• Define the vision and strategy for AI at Tivity Health—what problems we solve, how we prioritize, how we measure success
• Establish governance frameworks for responsible AI development (ethics, privacy, bias mitigation, compliance with healthcare regulations)
• Create the AI product playbook —templated processes for ideation, experimentation, evaluation, and deployment
• Build cross-functional relationships with data science, engineering, legal, compliance, and business leaders to align on AI priorities
• Identify and prioritize initial AI use cases across member engagement, care coordination, operational efficiency, and business intelligence
• Establish measurement frameworks —define KPIs, OKRs, and success metrics for AI initiatives; build dashboards and reporting cadences
• Own the end-to-end product lifecycle for AI initiatives—from discovery and requirements through design, build, launch, and iteration
• Partner with data science and engineering to translate business problems into technical solutions (predictive models, generative AI, automation, personalization)
• Define success metrics and KPIs for each AI product—track adoption, impact, ROI, and member/business outcomes
• Run experiments and pilots —test hypotheses, learn fast, kill what doesn't work, scale what does
• Ship production-ready AI features that directly improve member experiences (e.g., personalized recommendations, intelligent search, proactive outreach, chatbots)
• Own progress reporting for all AI initiatives—communicate status, blockers, wins, and risks to executive leadership and stakeholders
• Monitor and analyze performance against OKRs, KPIs, and success metrics—identify trends, surface insights, recommend course corrections
• Create compelling data stories —translate metrics into business impact narratives that demonstrate the value of AI investments
• Maintain visibility dashboards —build and maintain executive-level views of AI portfolio health, ROI, and strategic alignment
• Drive accountability —ensure teams are tracking the right metrics and making data-informed decisions throughout the product lifecycle
• Educate and inspire stakeholders across the organization—show what's possible, demystify AI, build excitement and trust
• Create compelling storytelling around AI wins—showcase ROI, member impact, operational improvements
• Build internal AI literacy —run workshops, create documentation, empower teams to think about AI opportunities
• Stay on the cutting edge —monitor AI trends, tools, and best practices; bring fresh ideas to the table

Requirements:

• 3-5+ years of product management experience, ideally with exposure to AI/ML, data products, or emerging technologies
• Proven track record of shipping 0→1 products—you've built something new from scratch and driven it to adoption
• Technical fluency—you understand how AI/ML works (models, training data, APIs, LLMs, etc.) and can have credible conversations with data scientists and engineers
• Strong strategic thinking—you can identify high-impact opportunities, prioritize ruthlessly, and build compelling business cases
• Experience with metrics and reporting—you're comfortable defining KPIs, building dashboards, analyzing performance data, and presenting insights to executives
• Bias for action: You move fast, iterate, and don't wait for perfect information
• Comfort with ambiguity: You thrive in unstructured environments and create clarity from chaos
• Cross-functional leadership: You influence without authority and build strong partnerships
• Data-driven decision making: You use metrics and insights to guide your choices
• User-centric mindset: You start with member/user needs and work backward
• Communication skills: You can explain complex AI concepts to non-technical audiences, tell compelling data stories, and rally people around a vision
• Executive presence: You're comfortable presenting to senior leadership, reporting on progress, and advocating for resources
• Explain the difference between supervised learning, unsupervised learning, and reinforcement learning
• Understand when to use predictive models vs. generative AI vs. rule-based systems
• Evaluate model performance (accuracy, precision, recall, F1 scores, etc.)
• Identify data quality issues and their impact on AI outcomes
• Articulate responsible AI principles (fairness, transparency, privacy, accountability)
• Navigate conversations about LLMs, prompt engineering, RAG, fine-tuning, and embeddings
• Healthcare or member-focused experience (preferred but not required)—bonus if you understand HIPAA, compliance, or regulated industries