IT BUSINESS PROCESS MANAGER

## IT BUSINESS PROCESS MANAGER

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- R&D DATA SCIENCEPresenta candidaturaremote type: Corporate & Support Functionslocations: SANT FELIU DE LLOBREGATtime type: Full timeposted on: Offerta pubblicata oggijob requisition id: 001564Stiamo costruendo il futuro della dermatologia medica concentrandoci sui bisogni ancora insoddisfatti dei pazienti e dando alle persone lo spazio per pensare in modo indipendente, assumersi responsabilità e generare un impatto che conta davvero.Il nostro purpose è semplice: trasformare la vita dei pazienti rispondendo a bisogni reali.

Lavoriamo con rigore, agiamo con coraggio, teniamo le cose semplici e orientiamo l’innovazione dove può davvero fare la differenza.Riconosciuti come Top Employer in Spagna dal 2008 e in Germania dal 2025, continuiamo a investire in un ambiente in cui le persone possono crescere e andare avanti.Se pensi in modo diverso, qui è il posto giusto per te.
MISSION
Act as the IT solution owner and trusted partner for the R&D Data Science team, collaborating closely with bioinformatics, molecular modelling, and AI Engineering to ensure alignment, proximity, and effective day-to-day collaboration.

Own and support Data Science applications, both internally developed and third-party, ensuring business alignment, prioritization, and adoption.

The role is accountable for end-to-end coordination, while execution of operations, architecture, security, compliance, and support is delivered in collaboration with specialized IT teams and external vendors.

This role serves as a ring-fenced resource dedicated to Data Science needs, enabling faster decision-making, better communication, and more efficient delivery of IT services.

It requires a strong scientific background and solid experience in R&D Data Science environments.
TASKS AND RESPONSIBILITIES
Operational:
* Lead demand intake, service coordination and project delivery for Data Science-related IT needs in close collaboration with Data Science stakeholders, especially bioinformatics and modelling teams
* Manage software development lifecycle of custom Data Science tools supporting the acceleration of hypothesis generation and scientific insight creation
* Act as a Single Point of Contact (SPOC) in IT for Data Science users, reducing complexity by shielding business stakeholders from interacting with multiple IT teams and ensuring seamless coordination across infrastructure, security, data and application domains
* Partner with relevant IT domains to evolve technology capabilities for R&D Data Science, supporting scientific platforms, advanced computing, omics, multimodal analytics, molecular modelling and AI/ML in line with business priorities
* Translate, review, and validate Data Science requirements into actionable IT deliverables with clear scope, timelines, and responsibilities, while safeguarding business and end-user needs through a unified model for demand intake, backlog prioritization, issue management and adoption
* Coordinate with the appropriate governance, security and compliance functions to ensure Data Science solutions align with R&D and enterprise requirements, including AI ethics, monitoring and risk management
* Coordinate with service support teams and technology vendors to ensure technical solutions meet agreed functional and performance expectations
* Ensure that appropriate documentation, knowledge transfer and service transition are in place for handover to IT support teams
* Accountable for the update of data science system maps and software developed by data science that support R&D business operations
* Work with the relevant expert teams to ensure systems and pipelines meet required standards for backup, data integrity, security and GxP compliance
* Contribute to cost-efficient technology decisions aligned with the long-term R&D business strategy, in collaboration with the relevant IT and procurement stakeholders
* Define and track KPIs to measure solution performance, adoption and business value.
* Drive project management using agile methodologies and increase speed of delivery, reducing time to insight and enabling faster hypothesis generation in all possible stages of Data Science flat & self-organised model.
* Represent Data Science growth needs in IT planning to help ensure that platforms and services can scale appropriately for omics, advanced analytics and AI use cases
* Coordinate the design and evolution of the end-to-end architecture for the Data Science environment with enterprise architecture a