THE POSITION

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organizations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximizing these opportunities. The new Computational Sciences Center of Excellence (CS CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Data and Digital Catalysts (DDC) department within the CS CoE is a diverse, curious and action-driven team at the intersection of computation, engineering and science with ambition to advance our technical excellence. The focus of the team is on partnering with the informatics and scientific communities to create a computational and data ecosystem that powers scientific discovery and accelerates decision making. We aim to modernize our ability to acquire, store, link, share, find and analyze data across the organization through scalable and integrated solutions that truly make every data point count. Reporting to the Domain Head for Lab Workflows & Data, this Senior Product Leader will play a key role in defining and executing the strategy for a family of products that are critical for drug discovery research.
The Lab Workflows & Data domain provides the end-to-end digital backbone for research design, experiment management, assay data & insights, and integrated research solutions. It spans hypothesis and study design (protocol authoring, approvals, ELN linkage), experiment orchestration (intake, scheduling, LIMS/LES execution), and the full sample/material lifecycle with automation, provenance, and chain-of-custody. It delivers assay data & insights capabilities—standardized ingestion, processing/secondary–tertiary pipelines, QC, lineage, harmonization, and analytics-ready datasets—underpinned by governed research master data (BER with GUPRIs), shared ontologies, and interoperable APIs.
As the Principal Product Leader for Experiment Planning & Execution product family, you will set the strategy and deliver products that connect research design intent to lab execution at scale. You’ll partner with scientific leadership (core labs, investigator labs, research operations) to translate their needs into intelligent orchestration, sample/material lifecycle management, and LIMS/LES workflows. You will ensure that scientific context—e.g. Research hypothesis, experiment planning details —flows as structured metadata with unique identifiers (GUPRIs) and linked to the electronic notebook (ELN) records, so experiments run efficiently, compliantly, and yield reproducible, AI-ready data. Your roadmap will emphasize proactive intake and validation to minimize back-and-forth between investigators and lab staff, unlock deep integration with automation and analysis pipelines, and a streamline UX that reduces cycle time and rework. Success will be measured by faster request-to-result, clearer handoffs, complete lineage tracing, and quality and completeness of the assay datasets. You would establish tight stakeholder engagement and institutionalize disciplined change management.
This is an exciting opportunity to significantly impact and accelerate discovering diverse therapeutics that fundamentally make patients’ lives better. You will have the opportunity to work closely with Genentech’s top tier scientists and drug discovery experts.

KEY ACCOUNTABILITIES

• Set the product strategy for Experiment Planning & Execution product family to connect research design intent (hypotheses, models, controls, acceptance criteria) to lab operations at scale; own the roadmap across experiment orchestration, scheduling/capacity, and work assignment.
• Partner with scientific leadership and core labs to translate assay fitness-for-purpose and experimental variables into structured, validated requests and executable workflows; design intelligent experiment intake that pre-empts back-and-forth, required metadata, and inventory/automation availability.
• Evolve end-to-end sample/material lifecycle (accessioning, derivations/aliquots, chain-of-custody, storage, disposition) with complete provenance, entity identifiers (GUPRIs), and robust barcode/labeling standards.
• Improve LIMS/LES usability for bench scientists and lab operations—streamline forms and task flows, reduce clicks, and support instrument-adjacent and automation-aware execution.
• Integrate deeply with ELN (protocols/versions), master data, vocabularies/ontologies, lab automation, and analysis pipelines; steward consistent APIs, events, and versioned schemas/data contracts.
• Lead a cross-functional agile team (engineering, UX, science SMEs, analysts) through discovery delivery with clear OKRs, release plans, and adoption/change-management in partnership with RDE Office/OCM.
• Manage stakeholder alignment, risk/issue resolution, and a balanced backlog (near-term value and foundational capabilities); ensure adherence to research data access policies, security, and validation expectations.
• Establish and cultivate strong internal and external partnerships and relationships with extended collaborators in Computational Sciences CoE and beyond.

Please refer the Job description for details