Lead Technology Product Manager, AI Clinical Decision Support
$31,200–$31,200 year
On-siteChicago, Illinois, United States or Austin, Texas, United States
Job Summary
Drive discovery and delivery for AI-enabled clinical decision support capabilities, translating clinician and business needs into product requirements, experiments, and roadmap priorities. Partner with engineering, design, clinical, content, analytics, and commercial teams from discovery through launch to shape experiences that help users navigate evidence-based content with confidence. Define success metrics tied to adoption, engagement, trust, and workflow impact, using customer conversations and analytics to validate product direction. Balance customer value, usability, and responsible AI considerations while communicating tradeoffs across leadership. Own measurable outcomes that improve how clinicians ask questions and move from question to action.
Required Qualifications
- 5+ years of product management experience in healthcare, enterprise SaaS, content products, search, workflow, or data-driven products
- Strong product discovery and execution skills, including customer research, experimentation, roadmap development, and metrics-driven decision-making
- Experience translating complex customer problems into intuitive product experiences, clear requirements, and measurable outcomes
- Ability to partner closely with engineering, design, clinical, content, analytics, commercial, and customer-facing teams
- Strong communication, analytical thinking, stakeholder-management, and influencing skills
- Curiosity and learning agility around AI, automation, emerging technologies, and responsible product development
- Ability to operate in ambiguity and make sound product decisions in complex, regulated healthcare environments
- Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process
Desired Qualifications
- Experience with AI-enabled, data-driven, search, content, knowledge, or decision-support products
- Practical understanding of generative AI, retrieval-based systems, recommendation systems, workflow automation, or applied machine learning concepts
- Experience with clinical decision support, healthcare content, point-of-care tools, or professional-information products
- Familiarity with human-in-the-loop workflows, responsible AI practices, model evaluation, or trust and safety considerations
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