Chemistry Data Science Jobs (NOW HIRING)

The Opportunity:

We are seeking a Senior Product Manager, Chemistry to deliver products and capabilities that help Chemistry teams make faster, higher-confidence design and progression decisions in oncology-focused drug discovery.

This role will define and deliver the product strategy for Chemistry workflows, data products, and AI-enabled decision support using the right mix of internal product development, SaaS platforms, vendor partnerships, integrations, and RevCore capabilities. You will partner with medicinal chemists, synthetic chemists, computational chemists, analytical chemists, DMPK and Biology partners, Data Science, ML Engineering, Data Engineering, IT, and platform teams to turn complex Chemistry workflows into intuitive, scalable solutions that accelerate the Design-Make-Test-Learn cycle.

Own Chemistry product strategy

• Define the vision and roadmap for Chemistry products and capabilities across medicinal chemistry, synthetic chemistry, analytical chemistry, compound management, and the Design-Make-Test-Learn cycle.

• Build a Now, Next, Later roadmap from foundational compound data capabilities to self-service analytics, model-supported design, and AI-enabled decision support.

• Set success metrics tied to trusted compound data access, reduced manual data preparation, faster design cycles, compound progression decisions, and scientific adoption.

Shape product solutions around Chemistry workflows and decisions

• Understand workflows for medicinal chemists, synthetic chemists, computational chemists, analytical chemists, compound management teams, and cross-functional program teams.

• Design solutions around key decision moments such as compound design, analog selection, route selection, synthesis planning, SAR interpretation, multi-parameter optimization, compound triage, and program prioritization.

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

• Determine when to build, buy, partner, or integrate based on user needs, market capabilities, scalability, differentiation, interoperability, and long-term maintainability.

Establish trusted, reusable Chemistry capabilities and data products

• Partner with technical teams, vendors, and SaaS providers to deliver priority Chemistry capabilities across RevCore and core Chemistry platforms.

• Clarify trusted sources and systems of record for key Chemistry data, including compounds, structures, batches, lots, reactions, routes, analytical results, assay results, and calculated properties.

• Improve structured data capture, data quality, metadata, and usability across Benchling, D360, legacy CDD data, compound registration, analytical systems, inventory systems, and related Chemistry platforms.

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

• Enable self-service access, compound search, structure search, SAR exploration, semantic discovery, and "Ask your Chemistry data" experiences across priority datasets.

• Use modern AI, analytics, workflow, and low-code tools to prototype concepts, validate user needs, and de-risk ideas before full engineering investment.

• Partner with Data Science and ML Engineering to identify and deliver AI and GenAI use cases such as chemistry copilots, SAR summarization, analog search, compound profile generation, synthesis-aware design support, and automated annotation

• Drive rollout, adoption, 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, Chemistry Informatics, Cheminformatics, Scientific Data Platforms, or related roles within biotech, pharma, life sciences, or another research-intensive environment.

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

• Deep understanding of small molecule Chemistry workflows, including medicinal chemistry, Design-Make-Test-Learn, SAR analysis, compound progression, and multi-parameter optimization.

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

• Working knowledge of Chemistry data and systems, including compound registration, structures, batches, reactions, analytical data, assay result integration, Benchling, D360, CDD, ELN, or related informatics tools.

• Product judgment to evaluate build, buy, partner, and integration options based on user value, market maturity, scalability, interoperability, and maintainability.

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

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

• Ph.D., M.S., B.S., or equivalent experience in Chemistry, Medicinal Chemistry, Cheminformatics, Computational Chemistry, Bioinformatics, Computer Science, Engineering, Information Systems, or a related field.

Preferred Skills:

• Experience establishing V1 data products, digital products, or foundational capabilities in a fast-moving biotech, pharma, or research environment.

• Experience evaluating, implementing, or integrating SaaS platforms and vendor solutions for Chemistry, cheminformatics, analytics, or scientific workflow use cases.

• Experience with AI-enabled molecular design, model-supported design workflows, cheminformatics, analog search, SAR tools, knowledge graphs, or multi-parameter optimization products.

• Experience building self-service data access, compound search, structure search, SAR exploration, semantic discovery, natural language query, or "Ask your data" experiences for Chemistry users.

• Experience using modern AI, analytics, workflow, and low-code tools to prototype product concepts, validate user needs, and de-risk ideas before full engineering investment.

• Familiarity with chemistry data standards, molecular representations, controlled vocabularies, metadata standards, FAIR data principles, and 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 all matter. #LI-Hybrid  #LI-YG1