Lead Product Analyst, CDS Adoption & Engagement

Role overview Analyze how people use clinical decision support products and turn product data into practical recommendations. The role spans adoption, engagement, funnels, and usage patterns, with direct analysis of product telemetry and close partnership with teams responsible for product direction, design, engineering, and customer adoption.

Responsibilities - Define and maintain measures for adoption, engagement, funnels, and product usage. - Query usage data to study actions, journeys, drop-offs, cohorts, segments, and behavior patterns. - Investigate which interactions lead to meaningful user actions and where workflows lose momentum. - Build dashboards and recurring views that help teams track product performance. - Partner with product managers to set success measures for launches, pilots, early releases, and experiments. - Use findings to recommend improvements to roadmaps, user experience, content, and adoption plans. - Improve event instrumentation and metric definitions so product data is consistent and dependable. - Present analysis and recommendations clearly to technical, commercial, leadership, and customer-facing audiences.

Requirements - At least five years of experience in product, data, or business analytics, product operations, or a related area. - Ability to write SQL or use comparable query tools independently. - Experience analyzing adoption, engagement, funnels, click behavior, cohorts, segments, and usage patterns. - Comfort working with event-level telemetry, data models, and imperfect instrumentation. - Ability to turn open-ended product questions into structured analysis and actionable recommendations. - Experience creating dashboards, recurring reports, or self-serve analytics views. - Strong communication and collaboration skills across product, engineering, design, analytics, commercial, or customer success teams.

Nice to have - Experience with healthcare workflows, enterprise software, AI-enabled products, search, content, or decision support. - Familiarity with experimentation, A/B testing, event instrumentation, or customer journey analysis. - Experience with modern data platforms or business intelligence tools, and combining qualitative feedback with quantitative analysis.