Job Introduction
Scientific Data Management Lead
Location: Remote or Hybrid (Hoddesdon site)
Role Purpose
The Scientific Data Management Lead will be responsible for developing, maintaining and improving scientific data workflows, upload processes, templates and visualisation tools across Pharmaron projects.
The role will support scientists and project teams by enabling effective data capture, upload, integration, tracking and reporting across scientific data platforms, with a particular focus on payload, linker and conjugate-related project data. The postholder will also contribute to the ongoing evolution of Pharmaron’s combined data platforms by reviewing current processes, identifying improvement opportunities and managing the execution of agreed refinements.
Key Responsibilities
Scientific Data Uploads, Templates and Protocols
- Create, maintain and improve protocols and templates for data upload across Pharmaron projects.
- Develop standardised data upload templates to support consistent data capture and platform integration.
- Upload scientific data through agreed templates where customers or Pharmaron bench scientists have not yet been trained to do so.
- Ensure data uploads are accurate, complete, timely and aligned with agreed project requirements.
- Support users in understanding data upload processes and promote consistent use of approved templates.
- Troubleshoot upload issues and work with relevant stakeholders to resolve data formatting, mapping or quality problems.
Spotfire Template and Data Visualisation Support
- Customise Spotfire templates, dashboards and views to meet scientists’ project and assay-specific requirements.
- Adapt existing visualisation templates to incorporate data from new assays and evolving project needs.
- Support scientists in using Spotfire outputs to review, interrogate and interpret project data.
- Ensure visualisation outputs are structured, reliable and fit for scientific decision-making.
- Work with project teams to define reporting requirements and translate these into practical data views.
Data Import, Export and Collaboration Support
- Manage large dataset imports and exports at the beginning, during and end of collaborations.
- Coordinate data transfer activities in line with agreed project timelines and collaboration requirements.
- Support project start-up by ensuring appropriate data structures, templates and workflows are in place.
- Support project close-out by enabling accurate final data exports and handover of project datasets.
- Maintain appropriate documentation of data transfer activities, template versions and upload processes.
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.
Skills and Experience
Essential
- 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
- 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.