Senior Project Manager - Scientific Workspace & Research Data Platforms.
hace 15 horas
Marín
About us Todas las habilidades, cualificaciones y experiencia relevantes que necesitará un candidato seleccionado se enumeran en la siguiente descripción. For more than 20 years, our global network of passionate technologists and pioneering craftspeople has delivered cutting‑edge technology and game‑changing consulting to companies on the brink of AI‑driven digital transformation. Since 2001, we have grown into a full service digital consulting company with 5500+ professionals working on a worldwide ambition. Driven by the desire to make a difference, we keep innovating. Fuelling the growth of our company with our knowledge worker culture. When teaming up with Xebia, expect in‑depth expertise based on an authentic, value‑led, and high quality way of working that inspires all we do. At Xebia, we put 'People First'—committed to attracting diverse talent and fostering an inclusive, respectful workplace where everyone is valued for their contributions. We welcome all individuals and evaluate solely on the quality of their work and teamwork. About the role We are seeking a strategic and delivery‑focused Senior Project Manager to drive the planning, coordination, and execution of programmes built on our enterprise Scientific Workspace platform — a next‑generation, cloud‑native research data environment that powers AI/ML‑driven drug discovery at scale. Operating at the intersection of data engineering, life sciences research, and platform product management, you will serve as the connective tissue between data scientists, research biologists, cloud engineers, and senior stakeholders. You will own the delivery of high‑value data platform capabilities that directly accelerate how our scientists identify targets, generate hypotheses, and advance candidates into clinical development. This is a high‑visibility, high‑impact role suited to someone who combines rigorous project management craft with genuine fluency in scientific data ecosystems, knowledge graph technologies, and cloud‑scale research infrastructure. WHAT YOU'LL DO Own end‑to‑end delivery of cross‑functional workstreams within the Scientific Workspace platform, spanning data ingestion pipelines, semantic data layers, AI/ML enablement features, and researcher‑facing tooling. Build and maintain detailed programme plans, dependency maps, and milestone frameworks that give leadership clear visibility of progress, risks, and trade‑offs. Drive agile delivery practices across mixed teams of data engineers, data scientists, and platform engineers — facilitating sprint planning, backlog refinement, and retrospectives with rigour and pace. Translate ambiguous scientific and business requirements into clearly scoped, deliverable project briefs that engineering and data teams can execute against. Act as a knowledgeable partner to platform product owners, understanding the architecture and capabilities of the Scientific Workspace environment — including its data ingestion frameworks, knowledge graph structures, semantic query layers, and integration points with laboratory and genomics systems. Coordinate the onboarding of new data sources and research modalities into the workspace, managing intake processes, data quality reviews, and researcher acceptance testing. Support the design and governance of workspace metadata standards, ontologies, and data cataloguing approaches to ensure data is FAIR (Findable, Accessible, Interoperable, Reusable) across research domains. Collaborate with data engineering leads to track platform reliability, performance SLAs, and incident resolution, escalating issues that impact research productivity. Serve as the primary point of coordination between research scientists, data platform teams, IT infrastructure, and senior R&D leadership — translating priorities across these audiences with clarity. Manage interdependencies with adjacent programmes including AI/ML model development, genomics data platforms, and external data partnerships and collaborations. Prepare and present programme status reports, risk registers, and investment justifications to senior and executive stakeholders. Partner with external data providers, academic collaborators, and technology vendors to manage contractual deliverables, integrations, and data sharing agreements. Ensure all platform programme activities meet applicable data governance, privacy, and regulatory standards, including GDPR, GxP guidelines where relevant, and internal data security policies. Maintain comprehensive programme documentation including decision logs, change requests, RAID logs, and lessons‑learned registers. Champion ethical and responsible use of research data across the platform, embedding data quality checks and provenance tracking as standard programme deliverables. WHAT YOU BRING Essential Degree in Computer Science, Data Science, Bioinformatics, Life Sciences, or a related discipline; postgraduate qualification advantageous. 7+ years of progressive project or programme management experience, including at least 3 years delivering complex data platform or research technology programmes. Proven, hands‑on experience working with or managing projects on a large‑scale Scientific Workspace or research data platform environment — encompassing data pipelines, cloud infrastructure, and researcher‑facing analytical tools. Strong working knowledge of cloud‑based data architectures (AWS, Azure, or GCP), including data lakes, data catalogues, semantic search, and API‑driven integration patterns. Experience managing cross‑functional delivery in agile environments, with proficiency in tools such as Jira, Confluence, Azure DevOps, or equivalent. Demonstrated ability to engage credibly with both research scientists and technical engineers, bridging scientific context and delivery rigour. Excellent written and verbal communication skills, including executive‑level reporting and facilitation of complex multi‑stakeholder discussions. Preferred Familiarity with knowledge graph technologies, ontology management, or semantic data standards (e.g., RDF, OWL, SPARQL) used in research data contexts. Experience with AI/ML platform delivery, including model training infrastructure, feature stores, or research data annotation workflows. Background in pharmaceutical or biotech R&D data environments, with an appreciation of drug discovery workflows from target identification to IND filing. Relevant certifications: PMP, PRINCE2, SAFe Agilist, or equivalent. xsgfvud Familiarity with FAIR data principles, data mesh architectures, or research data management frameworks. #J-18808-Ljbffr
