O pozici
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 McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, 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.
Co budeš dělat
- owning product strategy, roadmap, and lifecycle for scientific and R&D platform products,
- driving discovery activities with researchers, scientists, and business stakeholders to identify product opportunities and validate solutions,
- translating complex scientific, bioinformatics, and technical requirements into actionable product plans and user stories,
- managing and prioritizing product backlogs using Jira, Confluence, and Jira Product Discovery,
- leading products through the full development lifecycle, from ideation and requirements validation to launch and adoption,
- collaborating closely with Engineering, Data Platform, Infrastructure, and Architecture teams to shape scalable solutions,
- defining product requirements, acceptance criteria, and success metrics while ensuring full traceability,
- facilitating workshops, design reviews, stakeholder alignment sessions, and executive-level presentations,
- driving user acceptance testing, release readiness, and launch activities,
- supporting modernization, rationalization, and optimization initiatives across existing application portfolios,
- working with cloud-native platforms and data-intensive systems built on GCP,
- partnering with quantitative biologists, bioinformaticians, data scientists, and software engineers to deliver impactful scientific software solutions,
- identifying opportunities for AI and GenAI adoption across scientific and R&D workflows,
- leveraging AI-powered tools (e.g. Gemini, Atlassian AI) to improve prioritization, documentation, delivery efficiency, and product insights,
- monitoring product performance, user adoption, business value realization, and portfolio health.
Koho hledáme
- 8+ years of experience in Product Management, preferably within technical platforms, data products, or enterprise software,
- availability to collaborate with teams and stakeholders during UK and US East Coast business hours,
- strong experience managing technical products involving APIs, cloud platforms, data pipelines, or developer platforms,
- hands-on technical background with the ability to communicate effectively with engineers, architects, and data scientists,
- strong understanding of cloud technologies, with practical experience in GCP,
- experience working with services such as BigQuery, Cloud Run, Workflows, Vertex AI, or similar cloud-native solutions,
- excellent stakeholder management, requirements gathering, and business analysis skills,
- strong experience creating product requirements, user stories, and Product Development Plans,
- expertise in Agile product development and backlog management practices,
- advanced knowledge of Jira and Confluence,
- experience operating in complex, cross-functional environments with multiple stakeholders and dependencies,
- strong understanding of data ecosystems, integrations, authentication mechanisms (SSO, LDAP), and enterprise application architectures,
- experience working with AI/ML-enabled products, data platforms, or MLOps-related initiatives,
- deep professional experience within bioinformatics, computational biology, genomics, multi-omics platforms, or scientific data systems,
- familiarity with bioinformatics workflows, sequence analysis tools, and orchestration frameworks such as Nextflow, Snakemake, or Cromwell,
- practical knowledge of genomic datasets, repositories, and file formats including FASTQ, BAM, VCF, GFF, TCGA, NCBI, Ensembl, or UK Biobank,
- experience collaborating with computational biologists, bioinformaticians, statistical geneticists, or scientific research teams,
- practical experience using AI-powered assistants (e.g. Gemini, Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making.
