Job Details
- Status
- Active
- Posted
- Jun 15, 2026
- Expires
- Sep 13, 2026
- Work style
- Remote
About the Role
About this role:
Lyra Health is looking for a Lead Product Manager for Data and AI to define and drive the next generation of our data and artificial intelligence initiatives. In this role, you will lead the strategy, exploration, and end-to-end execution of complex product capabilities across our machine learning models and generative AI initiatives. You will work closely with product leadership, engineering, and cross-functional teams to make data a core enterprise asset that optimizes global workforce clinical operations, elevates the member experience, and expands our competitive differentiation.
As a critical member of the Product team, you will establish product operating rhythms, define multi-year vision, and translate complex clinical or business demands into scalable data and AI strategies. You will partner deeply with Data Engineering, Data Science, AI Infrastructure, and AI/ML Engineering teams, ensuring all product capabilities follow rigorous clinical evidence, data quality standards, and ethical, responsible AI practices.
Responsibilities
Product Vision & Strategy: Define and champion a compelling multi-year product vision and roadmap for data and AI platforms, aligning technical milestones with Lyra's business growth, revenue objectives, and clinical goals.
GenAI & Modern Lifecycle Management: Drive high-impact opportunities leveraging generative AI, Large Language Models (LLMs), agentic AI, and advanced analytics, managing the entire lifecycle from ideation and rapid experimentation to scalable production deployment.
Cross-Functional Leadership: Collaborate extensively across complex matrixed stakeholder groups—including Product, Design, Engineering, Clinical Quality, and GTM teams—to embed smart AI capabilities directly into core member experiences and operations.
Responsible AI & Governance: Partner closely with Legal, Privacy, and Clinical leadership to maintain rigorous responsible AI practices
Impact Metrics & Optimization: Design robust measurement and experimentation frameworks (such as online controlled A/B testing) to track model effectiveness, quantify business outcomes, and evaluate trade-offs between tactical execution and long-term research.
Technical Team Collaboration: Translate unstructured business pain points into crisp, detailed product requirements, acting as a translator between highly technical data disciplines and non-technical business leaders.
Mentorship & Culture: Lead by example, mentoring junior team members, sharing technical product management best practices, and actively fostering a highly rigorous, data-driven culture across the product organization.
Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related highly quantitative field.
Product Experience: 8+ years (Lead PM) of progressive product management experience within successful enterprise SaaS, cloud-based software, or health tech organizations, with a proven history of launching large-scale products with complex dependencies.
Data & AI Expertise: 3+ years of direct experience shipping machine learning and data-driven products at scale. Deep understanding of modern machine learning algorithms, deep learning techniques, generative AI use cases, and the infrastructure needed to deploy LLMs contextually.
Communication Skills: Exceptional written and verbal communication, narrative storytelling, and presentation skills; masterful ability to communicate complex algorithmic systems and technical trade-offs to non-technical executive teams and demanding corporate customer audiences.
Strategic Execution: Proven capacity to methodically unpack ambiguity, handle competing objectives, establish clear product boundaries, and maintain a calm, execution-oriented mindset in a fast-paced, high-velocity environment.
Healthcare Alignment (Nice-to-Have): Prior experience navigating a highly regulated industry (Healthcare Tech, FinTech) with an innate respect for data ethics, privacy constraints, and patient clinical quality.
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