About Xebia
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. We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture. In the CEE region, we're a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.
Responsibilities
•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.
Requirements
•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.
Recruitment Process
CV review – HR call – Interview – Client Interview – Decision
