Senior Product Manager, Biology

The Opportunity:

We are seeking a Senior Product Manager, Biology to shape products and capabilities that help Biology and Discovery teams design, execute, analyze, and learn from experiments faster, with trusted data and AI-enabled support.

This role will define and deliver product strategy for Biology workflows, data products, and AI-enabled decision support on RevCore, our enterprise Data, Digital, and AI platform. The mandate is to improve experiment traceability, reduce manual data preparation, accelerate cross-study analysis, and make Biology insights easier to generate and act on.

You will partner with scientists across Protein Science, Structural Biology, Screening Sciences, Sample Management, In Vivo Research, Pathology, Translational Research, Computational Biology, Data Science, ML Engineering, Data Engineering, IT, and platform teams to turn complex research workflows into intuitive, scalable products. Product surfaces may include experiment planning workflows, assay and screening result review, sample and reagent lineage, cross-study analysis, and “Ask your Biology data” experiences.

Own Biology product strategy and measurable outcomes

• • Define the vision and roadmap for Biology products and capabilities across Protein Science, Structural Biology, Screening Sciences, In Vivo Research, Pathology, Translational Research, and related discovery workflows.

  • Build a Now, Next, Later roadmap from foundational Biology data products to self-service analytics, workflow applications, and AI-enabled decision support.

  • Set success metrics tied to experiment traceability, data capture quality, data preparation time, result interpretation cycle time, scientific adoption, and program decision support.

  • Prioritize capabilities that reduce manual scientific workflows, improve data reuse, increase confidence in results, and scale across programs and research teams.

Shape solutions around Biology workflows and decisions

• • Understand workflows for wet-lab scientists, protein scientists, structural biologists, screening scientists, in vivo scientists, pathologists, translational scientists, computational biologists, and program teams.

  • Design solutions around key decision moments such as construct selection, assay design and interpretation, screening cascade analysis, hit or lead characterization, in vivo study review, pathology readouts, cross-study comparison, translational insights, and program prioritization.

  • Translate Biology workflows into clear product requirements, user stories, evaluation criteria, and prioritized capabilities.

  • Determine when to build, buy, partner, or integrate based on user value, scientific need, tool maturity, scalability, interoperability, and maintainability.

Establish reusable Biology data capabilities

• • Partner with technical teams, scientific system owners, vendors, and platform teams to deliver priority Biology capabilities across RevCore and core research platforms.

  • Clarify systems of record and reusable data products for key Biology data, including samples, reagents, constructs, assay results, screening data, structures, methods, study results, imaging, pathology readouts, and translational datasets.

  • Improve data quality at the point of capture across ELN, LIMS, assay and screening systems, imaging, pathology, workflow, and analysis platforms through better metadata, QC, annotation, and usability patterns.

  • Ensure Biology capabilities turn scientific, experimental, imaging, translational, and computational data into decision-grade insights, not just searchable records or dashboards.

Enable self-service discovery, AI use cases, and adoption

• • Enable self-service access, search, semantic discovery, cross-study analysis, and “Ask your Biology data” experiences across priority datasets and platforms.

  • Use modern AI, analytics, workflow, and low-code tools to prototype concepts, validate user needs, and de-risk ideas before larger product, platform, or vendor investments.

  • Partner with Data Science and ML Engineering to identify AI and GenAI use cases such as scientific copilots, experiment summarization, automated annotation, assay interpretation support, screening insights, cross-study analysis, and workflow automation.

  • Drive rollout and continuous improvement through usage metrics, feedback loops, training, and measurable workflow improvements.

Required Skills, Experience and Education:

• 8+ years of experience in Product Management, Data Product Management, Research Informatics, Scientific Data Platforms, Bioinformatics, Computational Biology, or related roles within biotech, pharma, life sciences, or another research-intensive environment.

• Strong product leadership experience defining vision, shaping strategy, building roadmaps, prioritizing tradeoffs, and delivering measurable outcomes.

• Deep understanding of Biology research workflows across domains such as Protein Science, Structural Biology, Screening Sciences, In Vivo Research, Pathology, Translational Research, or related discovery functions.

• Experience translating scientific workflows into scalable product capabilities, user stories, evaluation criteria, and product requirements.

• Working knowledge of Biology data and systems, including experimental metadata, assay and screening data, sample and reagent data, structural, imaging, in vivo, pathology, translational, ELN/LIMS, and downstream analysis workflows.

• Technical fluency across data platforms, data integration, analytics, data quality, governance, metadata, ontologies, and interoperability practices.

• Experience with research systems such as ELN, LIMS, assay platforms, screening systems, imaging systems, pathology systems, scientific workflow tools, analysis platforms, and related informatics systems.

• Strong communication and stakeholder management skills across scientific, computational, technical, vendor, and business teams.

• Ph.D., M.S., B.S., or equivalent experience in Life Sciences, Biology, Bioinformatics, Computational Biology, Computer Science, Engineering, Information Systems, or a related field.

Preferred Skills:

• Experience evaluating, implementing, or integrating SaaS platforms, scientific workflow tools, screening platforms, imaging/pathology platforms, analysis platforms, or vendor solutions for Biology research use cases.

• Experience enabling scientific data foundations for advanced analytics, machine learning, GenAI, scientific copilots, knowledge graphs, or decision-support products.

• Experience building self-service data access, search, semantic discovery, cross-study analysis, natural language query, or “Ask your data” experiences for scientific users.

• Experience using modern AI, analytics, workflow, and low-code tools to prototype product concepts, validate user needs, and de-risk ideas before larger product, platform, or vendor investments.

• Familiarity with FAIR data principles, scientific ontologies, metadata standards, knowledge management, or scientific data interoperability approaches.

• Comfort operating in an emerging biotech environment where strategy, execution, ambiguity, evolving scientific needs, vendor complexity, and hands-on problem solving are part of the work.

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