FAIR_Trainer

Trainers design and deliver training programs to a variety of stakeholders ranging from researchers to business leaders and project teams across organisation functions (R&ED, but also development, clinical, franchises), with a focus on awareness and best practice implementation of FAIR data principles. The role supports compliance with corporate data governance policies, regulatory expectations, and scientific needs. It interfaces and interacts with data governance leads, technology leaders, data architects, community managers, and researcher teams to align training content with organizational FAIR maturity and priorities.

Collaborate with data governance leads, technology leads, data architects, community managers and researcher teams to identify training needs aligned with corporate strategy; design tailored, role-specific training schedules; create high-quality, compliant, and FAIR-aligned learning materials (presentations, manuals, e-learning modules, job aids); deliver in-person, virtual, and hybrid sessions combining policy awareness with hands-on skills; promote company-approved and community recommended tools, standards, and workflows; adapt delivery for varied therapeutic areas, modalities, and stages of development (R&D, etc); establish continuous improvement processes through structured feedback and impact measurement. Keep track of FAIR training offering outside of the organisation.

Pains/Downside

The lower the organisation FAIR maturity level, the higher the complexity of the role and the need to create awareness. Lack of clarity and alignment in terms of needs and requirements from the organisations can be expected. In a change management process, the value of the training, FAIR and data-centric approach may be challenged.

Gains/Upside

Success stories give Trainers a concrete "why" to anchor every session, while open standards and real examples from FAIR implementation journeys and profiles make the content credible rather than theoretical. Because the training artefacts themselves are built FAIR, they're easy to reference, reuse, and share — across teams, and even beyond the organization.

FAIR principles provide a strong foundation for trainers to embed best practices in data management and reusability across teams and organizations. By leveraging success stories, FAIR Trainers can effectively articulate the underlying rationale ('why') for FAIR training, fostering greater engagement and commitment. The re-use of FAIR-aligned training materials and the adoption of open standards are facilitated by highlighting real-world examples from FAIR implementation journeys and profiles. With well-defined metrics and frameworks such as FAIR indicators and the FAIR Maturity Model (FAIR MM), Trainers are able to assess both datasets and organizational adherence, offering tangible benchmarks for progress. The impact of training is ultimately heightened through the availability of FAIR-compliant data and implementation examples.