Senior Scientific Product Manager

The Opportunity

• 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.

Who You Are

The ideal candidate will satisfy many of the following 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.

Preferred Qualifications

• 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).