Data Stewardship Manager

We’re building the future of medical dermatology by focusing on unmet patient needs and giving people the space to think independently, take ownership and make an impact that matters.

Our purpose is simple: to transform patients’ lives by addressing real needs. We work with care, act with courage, keep things simple and focus our innovation where it makes a difference.

Recognised as a Top Employer in Spain since 2008 and in Germany since 2025, we continue to invest in an environment where people can grow and move forward. 

If you care differently, you belong here. 

MISSION

As a Data Stewardship Manager you will join the Analytical & Formulation Sciences team within the Molecules organisation in Almirall R&D. You will drive and advance the organisation's digital and data strategy, by representing Molecules in shaping the Almirall R&D digital and data roadmap, partnering closely with scientists, laboratory teams, Data Science, and IT/Digital to translate scientific needs into aligned, scalable digital solutions. The role will build the trusted scientific data foundation needed to accelerate R&D, strengthen decision-making, and unlock future digital and AI-enabled capabilities.

CORE RESPONSIBILITIES

• Represent the Molecules organisation in shaping and implementing the broader R&D digital and data roadmap, ensuring its scientific needs, priorities, and workflows are incorporated into scalable digital solutions.
• Lead scientific workflow and data discovery sessions with laboratory teams, mapping experimental processes, data flows, calculations, decision points and exceptions to identify opportunities for standardization, automation and reuse.
•   Drive adoption of agreed data standards and digital workflows by developing guidance, training users, supporting onboarding and acting as a trusted partner to laboratory teams during changes in tools and ways of working.
• Partner with IT/Digital, Data Sciences, and laboratory teams to translate the R&D digitalization roadmap into harmonised data structures and practices across analytical techniques, therapeutic modalities, and laboratories.
• Establish and coordinate the stewardship and governance of analytical and drug development data generated by internal laboratories and external partners, providing the trusted data foundation required to deliver the R&D digitalization roadmap.
• Define and maintain data standards, metadata, data dictionaries, taxonomies, controlled vocabularies, critical data elements, and FAIR principles.
• Translate scientific needs into data and system requirements for ELN, LIMS, repositories, and other digital solutions.
•   Ensure data practices remain lean and efficient in non-GxP environments while meeting applicable data integrity, quality, regulatory, and GxP requirements where needed.
• Enable automation, advanced analytics, and AI through fit-for-purpose scientific data.
• Coordinate effectively with IT/Digital, Data Science, Quality, Regulatory, and other R&D stakeholders.

REQUIRED EDUCATION AND EXPERIENCE

• Bachelor’s or Master’s degree in Pharmaceutical Sciences, Biochemistry, Data Science, or a related discipline is desirable.
• Experience in pharmaceutical or BiotechMI R&D, analytical development, laboratory informatics, or scientific data management ecosystems is a must.
• Working knowledge in programming languages (e.g. Python, SQL etc.) is desirable.
• Ability to manipulate, analyse, and automate scientific data workflows is a must.
• Strong understanding of analytical laboratory workflows and associated scientific data.
• Practical knowledge of data governance, including metadata, data quality, data lineage, data ownership, taxonomies, and data lifecycle management.
• Experience translating scientific requirements into structured data and technology requirements.
• Strong communication, problem-solving, facilitation, and stakeholder-management skills, with the ability to explain complex scientific and data concepts to laboratory, technical and senior leadership audiences.

PREFERRED SKILLS & COMPETENCES

• Experience with laboratory systems such as ELN, LIMS, CDS, SDMS, or scientific data repositories.
• Familiarity with analytical techniques and their associated data.
• Experience with data harmonisation, migration, integration, or laboratory-system implementation.
• Understanding of FAIR data principles, pharmaceutical data integrity, and GxP expectations.
• Exposure to automation, advanced analytics, AI, and machine learning in scientific environments.
• Scientific mindset combined with a pragmatic, collaborative, innovative, and change-oriented approach.
• Fluency in spoken and written English is essential, other languages are a plus.