Senior Technical Product Manager – Human Biology

Responsibilities

• own and prioritize product backlogs across multiple technical workstreams, including human biological data repositories, analytical pipelines, and search and visualization platforms,
• lead strategic programs through product development lifecycle governance, from strategy and requirements validation through architecture review and launch,
• translate complex scientific and business requirements into clear user stories, acceptance criteria, product development plans, and actionable technical requirements,
• coordinate dependencies across data engineering, platform engineering, UI/UX, AI/ML, and scientific knowledge engineering teams,
• drive continuous discovery, user research, and feedback loops with scientists and R&D stakeholders to shape product roadmaps,
• define and maintain data models, metadata standards, FAIR data requirements, and data governance practices together with scientific and data teams,
• ensure security, compliance, data provenance, and traceability requirements are addressed throughout the product lifecycle,
• collaborate with Project Managers and Product Owners on scope, priorities, resource allocation, risks, and cross-team dependencies,
• drive UAT, release decisions, and launch readiness across technical, business, and scientific stakeholders,
• act as a primary point of contact between engineering teams, business stakeholders, scientific users, and external partners,
• facilitate requirements workshops, discovery sessions, design reviews, and cross-functional delivery meetings,
• present technical, product, and delivery updates to senior stakeholders and governance forums,
• use Jira, Confluence, and Jira Product Discovery to manage backlogs, requirements, documentation, and traceability,
• partner with engineering and infrastructure teams on cloud architecture, APIs, distributed data flows, and scientific data ingestion,
• leverage GCP services and modern data engineering capabilities to support scalable scientific data platforms,
• identify opportunities for AI and automation across program management, requirements, documentation, and scientific workflows,
• support the identification and delivery of production AI use cases together with client teams,
• drive continuous platform improvement, modernization, technical debt reduction, and cost optimization,
• coordinate external vendors and technology partners where required.

Requirements

• expert knowledge of Jira and Confluence, with experience maintaining complex backlogs and requirements traceability,
• excellent stakeholder management, communication, facilitation, and conflict resolution skills,
• strong analytical and strategic thinking, with the ability to navigate complex and ambiguous requirements,

Preferred

• experience with human data governance, patient privacy, data reproducibility, lineage, and audit readiness will be a plus,

Your profile

• 8+ years of experience in technical program management, product management, technical delivery, or a similar role, ideally involving cloud-based data platforms or scientific informatics systems,
• proven experience leading large, cross-functional programs involving multiple engineering teams and business or scientific stakeholders,
• strong background in life sciences, biopharma, biotech, or a closely related scientific domain,
• ability to communicate fluently with cell biologists, computational biologists, data scientists, and engineering teams,
• strong understanding of human biological data and multi-omics data types, such as transcriptomics/RNA-seq, proteomics, and functional assays,
• practical knowledge of FAIR data principles, biological ontologies, metadata standards, and data governance,
• experience working in pharmaceutical or other highly regulated environments, with knowledge of GxP and/or 21 CFR Part 11,
• strong technical background in software engineering, data engineering, technical architecture, or platform development,
• solid understanding of data pipelines, APIs, distributed systems, databases, and modern cloud platforms,
• hands-on experience with GCP is required, including familiarity with services such as BigQuery, Workflows, Vertex AI, Cloud Run, and Google Cloud Storage,
• experience managing systems that ingest, process, store, and expose complex scientific or other large-scale datasets,
• working knowledge of SQL and NoSQL databases and modern data engineering practices,
• familiarity with AI/ML platforms and MLOps concepts,
• strong understanding of Agile, Scrum, and hybrid delivery models,
• experience with high-performance computing (HPC), large-scale data processing, or HPC-to-cloud migration will be a plus,
• knowledge of GCP architecture for large-scale scientific workloads and storage optimization will be a plus,
• experience with biological research workflows, in vitro cell models, high-throughput phenomics, or translational research will be a plus,
• experience with vendor management and multi-vendor delivery environments will be a plus,
• relevant GCP certifications, such as Professional Cloud Architect or Professional Data Engineer, will be a plus.

CV

review –

HR

call –

Interview

Client

Interview –

Decision