Senior Product Manager, Real World Evidence (RWE) (Fully Remote; US-Based)

Genomenon, Inc

6d•Remote

About The Position

Genomenon is an AI-driven biomedical intelligence company on a mission to save and improve lives by making biomedical information actionable. Rare diseases affect more than 30 million people in the U.S. alone and hundreds of millions globally, yet most patients still face long diagnostic journeys and limited treatment options. Our goal is clear and ambitious: to deliver the information that shapes diagnosis and treatment for every rare disease patient. We turn vast, complex biomedical data — spanning genomics, clinical evidence, and scientific literature — into trusted intelligence that helps clinicians diagnose patients, enables researchers to make new discoveries, and supports life sciences organizations in bringing better therapies to market faster. Our work has real, measurable impact. Genomenon’s platforms and services are used by hundreds of clinical laboratories, healthcare organizations, and life sciences teams and pharma companies to support diagnostic interpretation, variant curation, and evidence-based decision-making and drug development. Each year, our technology helps inform care for tens of thousands of patients facing rare, complex, and time-sensitive conditions, reducing uncertainty and delivering answers when they matter most. What makes Genomenon unique is our ability to support both clinical diagnostics and pharmaceutical innovation on a shared foundation of advanced AI, deep domain expertise, and rigorously curated data. In the clinic, our solutions directly influence real-world patient outcomes. In pharma, we enable teams to harness literature-derived real-world evidence across discovery, development, and regulatory workflows — turning fragmented biomedical knowledge into a strategic asset. If you’re motivated by impact, energized by complexity, and excited to help shape the future of rare disease diagnosis and treatment, there’s no better place to do that work. We’re hiring a Senior Product Manager to own strategy and delivery for Genomenon’s Literature-derived Real-World Evidence (RWE) data products and software platforms supporting rare disease and cancer diagnostics and drug development. You will partner with executive, engineering, scientific, data, and commercial teams to define product direction, translate customer and market needs into clear priorities, and ship solutions that are scientifically credible, commercially valuable, and measurable in impact. This role is ideal for a strategic, hands-on product leader with experience in real-world evidence and advanced AI applications in biomedicine who thrives in a fast-paced environment and wants to build mission-driven technology that improves patient outcomes.

Requirements

Proven leadership with the ability to earn trust and drive team performance
Highly organized with strong project and time management skills
Skilled communicator across executive, engineering, and peer teams
Strategic thinker with strong analytical and problem-solving capabilities
Results-oriented with a strong sense of ownership and accountability
Bachelor’s degree in a relevant field (e.g., Life Sciences, Computer Science, Engineering, Business, or a related discipline)
5–10+ years of product management (or equivalent) work in a software-, data-, or AI-driven environment
Demonstrated ability to turn complex scientific or data workflows into clear, scalable, and defensible customer offerings
Proven track record of building or supporting AI/ML-enabled products, including LLM-based tools
Strong domain background in healthcare, genomics, clinical diagnostics, pharma, or health IT, with particular familiarity with real-world evidence data products
Demonstrated fluency working with structured biomedical data, such as patient records, genetic data, and variant interpretation outputs
Proven ability to manage products that combine algorithmic outputs with expert human review and/or annotation workflows
Familiarity with real-world evidence generation, interpretation, and use in regulated or semi-regulated pharma environments
Ability to clearly communicate AI-derived insights, limitations, and validation approaches to non-technical stakeholders (e.g., pharma customers, executives, regulators)
Exceptional ability to translate across technical, operational, and business contexts, aligning teams and stakeholders around shared outcomes
Working knowledge of agile product development methods and tools (e.g., Jira, Confluence, Trello), plus familiarity with project management platforms (e.g., Asana, Monday.com, Smartsheet, Microsoft Project)
Strong data fluency, including defining and tracking KPIs, using metrics to guide decisions, and sharing insights in plain language
Demonstrated ability to lead through change in fast-moving environments, staying resilient and bringing others along
Deep capability in customer discovery, including user research, persona development, and market and competitive analysis
Proven ownership of release readiness, including QA/UAT practices and continuous improvement of testing processes

Nice To Haves

Advanced degree in a life science, health IT, business, or technical field (e.g., MBA or PhD)
Training or certification in product (e.g., Pragmatic Institute, AIPMM, CSPO/PSPO) or project management (e.g., PMP, PMI-ACP, SAFe, Scrum Master) is a plus

Responsibilities

Own strategy and delivery for AI-extracted real-world evidence (RWE) products and expert-curated RWE datasets.
Lead customer discovery, market research, and competitive analysis to shape roadmap and differentiation.
Partner with Marketing and Commercial on go-to-market plans, messaging, and sales enablement.
Write and maintain product requirements (PRDs/specs), user stories, and roadmaps; drive sprint-level execution with Engineering/AI leaders.
Define clear product positioning, boundaries, and customer expectations across AI insights and curated data.
Set quality standards for AI outputs (validation, confidence signals, acceptance thresholds) with AI, Science, and Curation leaders.
Decide where to automate vs require expert review based on use case, regulatory sensitivity, and scientific risk.
Ensure release readiness, including user acceptance testing for major and minor releases.
Build and maintain an integrated roadmap that balances innovation, commercial potential, strategic alignment, and scientific rigor.
Translate emerging AI capabilities into trusted, commercially viable pharma offerings.
Develop packaging and tiering for different pharma use cases (discovery, translational, clinical, medical affairs).
Communicate product vision and progress to executives and cross-functional stakeholders.
Establish governance and documentation for AI-driven data products (provenance, assumptions, limitations, review cycles).
Support customer diligence, audits, and technical deep dives with Scientific and Commercial teams.
Define and track KPIs (revenue, adoption, delivery) and use them to drive prioritization.
Improve operating cadence and cross-functional collaboration across teams.