Project Data Infrastructure and Process Improvement

• Review current project data handling processes, with emphasis on coordination of payload, linker and conjugate data.
• Identify gaps, inefficiencies or risks in existing data workflows and recommend practical improvements.
• Propose plans for refinement of data platform implementation and project data infrastructure.
• Manage the execution of approved changes to data workflows, templates and visualisation tools.
• Contribute to the evolution of Pharmaron’s combined data platforms through research, planning, testing and implementation activities.
• Support integrated SXR tracking by improving coordination and connectivity of relevant scientific datasets.

Stakeholder Engagement and User Support

• Act as a key point of contact for scientific data system and template-related queries.
• Work closely with bench scientists, project teams, informatics colleagues and customer-facing teams.
• Gather user requirements and translate scientific needs into effective data templates, workflows and visualisation outputs.
• Provide guidance and informal training to scientists and project users on data upload processes and platform use.
• Build strong working relationships across teams to support consistent, high-quality project data management.

Key Deliverables

• Standardised and fit-for-purpose data upload templates across relevant Pharmaron projects.
• Accurate and timely data uploads where user training or capability is not yet in place.
• Customised Spotfire views and templates aligned to scientific and project requirements.
• Efficient large-scale dataset imports and exports for collaboration start-up and close-out.
• Improved workflows for payload, linker and conjugate data coordination.
• A documented plan for refinements to conjugate project data infrastructure.
• Successful execution of agreed changes to support integrated SXR tracking.
• Clear documentation of protocols, templates, processes and platform updates.

Essential Skills and Experience

• Experience working with scientific, research or laboratory data.
• Strong understanding of data handling, data quality, data upload processes and structured templates.
• Experience supporting scientific data systems, data platforms or informatics tools.
• Experience creating or maintaining data templates, protocols or standard operating processes.
• Ability to work with large datasets and manage data imports and exports accurately.
• Experience using data visualisation or analytics tools, ideally Spotfire.
• Strong Excel or equivalent data manipulation skills.
• Basic scripting, data transformation or workflow automation experience.
• Excellent attention to detail and a methodical approach to data review and problem solving.
• Ability to communicate effectively with scientists, technical specialists and project stakeholders.
• Ability to understand scientific user requirements and translate them into practical data solutions.

Desirable Skills and Experience

• Experience in a CRO, pharmaceutical, biotechnology or drug discovery environment.
• Knowledge of conjugates, ADCs, payloads, linkers, conjugate characterisation or related scientific datasets.
• Experience supporting assay data integration and visualisation.
• Familiarity with laboratory data systems, ELNs, LIMS, data warehouses or scientific informatics platforms.
• Experience contributing to system implementation, process improvement or data governance initiatives.
• Experience providing user support, training or guidance to scientific teams.

Qualifications

Essential

• Degree in chemistry, biology, biochemistry, pharmaceutical sciences, bioinformatics, data science, scientific informatics or a related field.

Desirable

• Additional training or certification in data management, data visualisation, Spotfire, informatics systems or project management.

Competencies

• Strong data accuracy and quality mindset.
• Scientific curiosity and ability to understand project context.
• Clear communication and stakeholder management skills.
• Practical problem-solving ability.
• Process improvement mindset.
• Ability to manage multiple priorities across projects.
• Collaborative working style.
• Confidence working between scientific, data and technical teams.
• Ability to document processes clearly and consistently.
• Proactive approach to identifying and implementing improvements.