181549 Research Assistant in Health Data Sciences
Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS)
Grade 6: £34,982 - £38,674 per annum, pro rata. This is a part time (50% FTE, minimum of 30% will be considered), fixed term position for 12 months in the first instance.
We are seeking to recruit a Research Assistant in Health Data Sciences to join the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), based at the Botnar Research Centre, Oxford.
The NDORMS Pharmaco- and Device epidemiology research group is involved in a number of national and international studies exploring the conditions of use (adherence, compliance, off and on-label use) of a number of drugs, procedures and devices for the treatment of various conditions in 'real world' actual practice conditions. We collaborate with researchers from around the globe to improve our understanding of the safety and effectiveness of new and long-licensed medications, vaccines, and devices. As a Research Assistant in Health Data Sciences in the group you will be supporting analyses of real-world health care data by writing analytic study code and contributing to our open-source software packages.
As a Research Assistant, your primary duty will be to support the analysis of real-world data mapped to the OMOP Common Data Model, contribute to wider project planning, including ideas for new research projects and determine the most appropriate methodologies to test hypotheses, and identify suitable alternatives if technical problems arise. You will contribute to scientific reports and journal articles and the presentation of data/papers at conferences as well as maintaining effective communication with members of the research team working with staff in other collaborating centres as required.
You will hold a relevant post-graduate degree in Mathematics, Engineering, Health Data Sciences or Biostatistics, have experience in statistics and/or health data sciences and have knowledge of programming in R and/or Python. You will be self-motivated with proven organisational and time management skills and have the ability to work within multi-disciplinary teams and independently.
Experience in the analysis or interpretation of OMOP-mapped data and working within an academic environment is desirable but not essential.
The closing date for this position is 12 noon on Friday 5 September 2025. You will be required to upload a CV and supporting statement as part of your online application.
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