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# Research Data Officer (Epidemiology)

- **Company:** ICR
- **Location:** Sutton, UK
- **Salary:** £39,805.0 - £50,862.0
- **Contract:** Temporary contract
- **Skills:** Computer Programming, Data Structures, Interoperability, Python (Programming Language), Data Processing, Data Ingestion, Data Analytics, Data Management
- **Published:** June 17, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=cb6d5c8d317ad4f7

## About the Role

Do you have a Master's degree?, The successful candidate will work closely with data engineers to support data ingestion, structuring, and storage. Will collaborate with analysts and researchers to understand analytical needs and ensure datasets are fit for purpose, and must have:

 * MSc in epidemiology, data science, biostatistics, or a related quantitative field
 * A strong track record of experience working with complex, epidemiological datasets, including programming for data manipulation (R, Python, others)
 * Strong understanding of data structures and data management in research settings, with attention to data quality, documentation, and reproducibility
 * The essential skills as detailed in the person specification on the job description

## Description

Under the guidance of Professor Montserrat Garcia-Closas, Group Leader Integrative Cancer Epidemiology, Division of Genetics & Epidemiology, we are seeking to recruit an experienced, motivated and organised Research Data Officer to lead the development of high-quality, research-ready datasets for large-scale, integrative epidemiological research within the Generations Study.

This is an excellent opportunity for candidates interested in using innovative data-driven approaches, working closely with epidemiologists, data engineers, data scientists and biostatisticians, contributing to high-impact research on cancer risk and prevention, focusing on transforming complex, multi-modal data (including questionnaire, clinical, imaging, and biomarker data), using modern data methods following FAIR (Findable, Accessible, Interoperable, Reusable) principles to enable robust, reproducible research.


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