Senior Technical Product Manager (Casual Biology)

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.

You will be:

• Owning product strategy, roadmaps and value validation for causal biology products or related scientific data capabilities.
• Driving continuous discovery with researchers and R&D users through interviews, workflow observation and feedback sessions.
• Translating complex scientific and technical needs into Product Development Plans, clear user stories and testable acceptance criteria.
• Prioritising the backlog and maintaining requirements traceability in Jira, Confluence and Jira Product Discovery.
• Guiding products through strategy alignment, requirements validation, architecture and design reviews, and launch.
• Making scope and priority decisions, while coordinating with Project Managers on resources and dependencies.
• Leading UAT, release decisions and launch readiness with technical, business and scientific stakeholders.
• Working closely with engineering, data platform, bioinformatics and computational science teams on APIs, data flows, cloud platforms and integrations.
• Supporting product modernisation, rationalisation and adoption, and monitoring support requests against the product strategy.
• Exploring AI opportunities with client teams, including AI-supported prioritisation, analytics and requirements documentation.
• Presenting product updates and recommendations to business leaders and governance forums.

Your profile:

• 8+ years of product management experience, preferably with technical, platform or data products.
• Experience working closely with software engineering, data engineering or architecture teams.
• A strong understanding of product discovery, business use cases, user interviews, roadmaps and backlog prioritisation.
• Proven ability to map stakeholders, resolve conflicts and communicate with technical and non-technical audiences.
• Experience writing clear product requirements, PRDs or Product Development Plans.
• Practical knowledge of agile methods and hybrid delivery models.
• Strong Jira and Confluence skills.
• Ability to read technical documentation, follow data flows and discuss technical decisions with engineers and data scientists.
• Scientific credibility in life sciences, computational biology, bioinformatics, genomics, omics or a closely related field.
• Understanding of scientific research workflows and the needs of researchers and computational scientists.
• Knowledge of cloud platforms, APIs, data pipelines and data platforms.
• Working knowledge of SQL or NoSQL databases and familiarity with MLOps and AI/ML platform requirements.
• Availability to work during UK hours.
• Practical experience using AI-powered assistants (e.g. ChatGPT, Claude, Copilot Chat, or similar) to improve productivity, quality, or decision-making in analysis and software delivery.

Work from the European Union region and a work permit are required.

Nice to have:

• Experience in pharmaceutical or other regulated environments, including GxP or 21 CFR Part 11.
• Familiarity with scientific computing, high-performance computing (HPC) or HPC-to-cloud migrations.
• Experience with GCP, including HPC-related services and configurations.
• Knowledge of SSO, LDAP, zero-downtime deployments and large scientific data transfers.
• Experience with scientific data provenance, reproducibility, security and regulatory requirements.
• A background in AI or GenAI applications for scientific workflows.
• Experience with vendor relationships, multi-vendor delivery and enterprise integrations.
• A track record in product adoption, portfolio rationalisation, modernisation, technical debt reduction or cost optimisation.
• Familiarity with life sciences software such as Schrödinger or Benchling.
• Relevant cloud certifications.
• Experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily analytical and documentation work.
• Interest in and familiarity with emerging AI-driven practices (e.g. automation of analysis tasks, AI-supported documentation, or workflow optimization), with a willingness to explore and experiment beyond standard approaches.