Senior Product Leader, Therapeutics Discovery

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The Computational Sciences Center of Excellence (CS CoE) brings together scientific, data and computational capabilities to support research and early development across Genentech (gRED) and Pharma (pRED). Within CS CoE, Data and Digital Catalysts (DDC) bridges the informatics and scientific communities to create solutions that power scientific discovery and accelerate decision-making.

Reporting to the Therapeutics Discovery Domain Head, you will own a defined subset of scientific informatics products that enable assay request, setup, execution and experimental data capture. Your focus will be to make data and metadata capture an integral part of experimental work: establishing the required scientific context before data are generated, capturing it as early as possible, and preserving what actually happened during execution.

These products should support the original scientific purpose while making results interpretable, traceable and reusable for scientific analysis, decision-making and machine learning.

The remit spans therapeutic modalities, with an emphasis on adaptable workflows that can support all drug discovery modalities over time.

The Opportunuity:

As an integral member of this dynamic team, you will be:

• Product ownership and prioritization. Own the roadmap, prioritization and outcomes for assigned products within a product family. Contribute to family strategy and align delivery with scientific priorities, working with the Domain Head, product family leads and engineering partners.
• Assay setup and execution. Understand how scientists request, prepare and execute assays. Translate these workflows into practical product requirements that capture known experimental context during request and setup, minimize repeated entry, and update records with actual conditions, deviations and quality information as work proceeds.
• Scientific data and metadata capture. Partner with experimental scientists and computational users to define the data, identifiers, relationships and metadata needed to interpret and reuse results. Embed appropriate capture and validation into workflows, supporting scientific analysis and machine learning without making scientists reconstruct context after the experiment.
• Coordination with adjacent capabilities. Enable capture into existing laboratory information management systems (LIMS) or the shared data layer being built, in line with agreed architecture. Coordinate with instrument-capture and data-layer teams to keep experimental context linked to results.
• Requirements, delivery and adoption. Translate scientific needs into clear user stories, functional requirements and acceptance criteria. Work with engineering and scientific partners to evaluate usability, validate delivered capabilities and drive adoption. Develop reusable approaches while preserving meaningful differences across assays and modalities.
• Workflow quality and measurable outcomes. Within your product scope, prioritize improvements that reduce manual workarounds and strengthen workflow reliability and data quality. Define and track outcomes such as setup effort, metadata completeness and adoption. Partner with engineering and support teams on sustainable maintenance and transitions to replacement capabilities.

Who You Are:

We welcome experienced product leaders with substantial drug discovery understanding and drug discovery scientists who have moved into informatics or digital solution delivery. Scientific…