Senior Scientific Product Manager

Genentech•Daly City, CA

3d•$126,100 - $234,100•Hybrid

About The Position

Roche's Research and Early Development organizations (gRED and pRED) are leveraging advances in AI, data, and computational sciences to transform drug discovery and development. The new Computational Sciences Center of Excellence (CS CoE) is a strategic group focused on harnessing the power of data and AI to assist scientists in delivering innovative medicines. The Data and Digital Catalysts (DDC) department within the CS CoE partners with the informatics and scientific communities to create a computational and data ecosystem that powers scientific discovery and accelerates decision-making. This role, reporting to the Domain Head for Lab Workflows & Data, will define and execute the strategy for a family of products critical for drug discovery research. The Lab Workflows & Data domain provides the digital backbone for research design, experiment management, assay data & insights, and lab workflows, spanning hypothesis design to assay data processing and analytics. As a Senior Scientific Product Leader, you will lead an integrated product strategy for lab workflows, research data, and AI/ML enablement, translating scientific priorities into an outcomes-driven roadmap. You will focus on making high-quality, governed, reproducible, and reusable data a core product outcome, addressing identifiers, metadata, provenance, harmonization, and data quality to support scientific analysis and model reproducibility. You will partner with various stakeholders to translate research needs into prioritized capabilities, balancing foundational investments with high-value deliveries, and guiding capabilities from pilots to scaled adoption. This role offers an opportunity to significantly impact and accelerate the discovery of diverse therapeutics.

Requirements

5+ years of product leadership in life-sciences R&D (biotech/pharma, CRO, or research tech), including end-to-end ownership of a complex product area serving scientists and lab operations.
Demonstrated AI product leadership in scientific research. Experience translating scientific needs into prioritized AI/ML product capabilities, defining success criteria, and partnering with data/AI and engineering teams to move from use-case discovery through pilots and scaled adoption.
Able to connect AI product priorities to scientific outcomes and the data foundations required to achieve them.
Deep research-data expertise. Strong understanding of how assay outputs become high-quality, analysis-ready, and reusable datasets through ingestion, processing, QC, harmonization, and lineage/provenance.
Able to translate requirements for training-set curation, harmonized identifiers, and model reproducibility into actionable product priorities.
Strong systems and data-governance acumen. Experience with API-first and event-driven integration, data modeling and ontologies, master data management, FAIR data practices, metadata quality, and data stewardship.
Understand how project- and study-based access controls, auditability, and security/privacy-by-design support governed data sharing and reuse.
Track record of delivering measurable scientific and data outcomes, including cycle-time reduction, first-time-right execution, improved data and metadata quality, and increased reuse of entities and datasets.
Experience instrumenting products with telemetry and using evidence to guide prioritization, iteration, and adoption.
Skilled at stakeholder leadership across PIs, lab managers, assay owners, data/AI teams, product/engineering, and executive sponsors; excellent written and verbal communication tailored to scientific and executive audiences.
Comfortable operating in a matrixed, global organization; adept at change management, roadmap sequencing, and risk management for multi-team deliveries.

Nice To Haves

Advanced degree in a life-science discipline (PhD, PharmD, MD, MS) or comparable hands-on lab experience that enables fluent conversations with researchers and scientific staff.
Experience with lab automation ecosystems and instrument/data integration across key assay families (e.g., sequencing/omics, high-content imaging, flow cytometry, bioanalytical/PK/PD).

Responsibilities

Own an integrated workflow, data, and AI product strategy. Define the vision, strategy, and multi-year roadmap for lab workflows, research data, and AI/ML-enabling capabilities, aligned to scientific priorities and portfolio needs.
Make foundational data investments and their contribution to research outcomes explicit in roadmap decisions.
Own research-data capabilities as product outcomes. Deliver integrated workflows connecting study design, protocol execution, the sample/material lifecycle, assay outputs, and insights.
Define product requirements for standardized ingestion, processing, QC, metadata, lineage/provenance, and harmonization so that datasets are reproducible, reusable, and suitable for scientific analysis and AI/ML.
Lead AI product discovery and prioritization. Through continuous discovery with PIs, lab scientists, assay owners, and operations, identify and prioritize opportunities for AI/ML-enabled research capabilities.
Partner with data/AI and engineering teams to translate these opportunities into product requirements, data-readiness requirements, prioritized epics, and explicit success criteria.
Drive governed platform and data interoperability. Use API-first, event-driven, ontology-aligned patterns to connect ELN/LIMS, lab automation, instrumentation, and data platforms.
Ensure consistent use of GUPRIs, master data, and shared ontologies, with clear provenance, access controls, and auditability.
Lead change and adoption by establishing communication plans, training curricula, role-based onboarding, super-user networks, and feedback loops.
Orchestrate cross-functional delivery (product, engineering, data/AI, UX, QA/CSV, security, privacy, procurement/vendors); remove blockers, manage risks, and maintain a durable delivery cadence.