Key Elements and Responsibilities
ELN Design and Development
· Own the continuous improvement of the ELN platform, identifying and prioritising enhancements in line with evolving laboratory and data needs.
· Engage stakeholders across R&D laboratories, quality and lab informatics to capture and define their requirements for the ELN platform.
· Design and build the ELN tools required, including standard modules (e.g. Inventory and Registry), workflows and templates to support laboratory operations.
· Support the design and implementation of integrations between the ELN and other laboratory or business systems.
· Support the development of dashboards and custom label reports for monitoring of R&D data.
· Evaluate and trial new ELN features, such as in-built AI capabilities, to drive continuous improvement.
ELN Operations and Support
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Monitor and troubleshoot any issues that arise, ensuring the smooth operation of the ELN platform.
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Write technical documentation to support ELN usage.
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Deliver ELN training as required.
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Provide day-to-day support and technical guidance to the ELN superusers.
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Establish, implement and monitor the practices and standards that uphold data integrity and ensure compliance with internal ELN policies.
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Act as R&D’s representative and primary liaison for the ELN, with internal project teams, the broader Kyowa Kirin organisation and external parties such as the ELN vendor.
· Act as the primary liaison between TD and the consultants (data engineers), translating scientific data requirements into clear specifications for the design and delivery of a data repository.
· Lead the development and implementation of a centralised TD data repository that consolidates data from across the programme lifecycle (including CDMOs) and meets the data integrity requirements for CMC dossiers and regulatory filings.
· Collaborate across TD, Manufacturing Science and Technology, Quality and laboratory informatics to align on consistent data capture methods, data structures, nomenclature and shared data goals.
· Define the reporting and analytics needs required to enable data-driven scientific decision-making from the repository.
· Write SQL queries to extract, transform and validate data from laboratory systems and the data repository.