About the Role
You are invited to apply for the position of Data Analyst to join Professor James Chalmers’ research team at the University of Oxford.
Professor James Chalmers specialises in translational and clinical research on respiratory diseases, including bronchiectasis, COPD and pneumonia, with a focus on microbiology, immunology, disease mechanisms and therapeutic strategies. You will be expected to provide high quality technical assistance to Prof. Chalmers and the other members of the research team.
As a Data Analyst, you will work within a multidisciplinary team of researchers, medical professionals, study coordinators and collaborators on projects with direct patient impact. You will be supported by the group’s statistician and bioinformatician while assisting with data management and analysis for the EMBARC consortium (https://www.bronchiectasis.net/), translational studies, and wider group projects involving postdoctoral researchers and students.
You will provide analytical support, training, and guidance to members of the group on data handling, analysis workflows, and best practice in data management.
The successful candidate will be responsible for maintaining and organising research datasets within the team, and for preparing and distributing curated datasets for analysis by other members of the group. A strong understanding and application of data analysis principles, including data quality control, documentation, and reproducibility, will be essential.
You will apply established analytical methods, maintain awareness of their limitations and assumptions, and ensure high standards of quality and accuracy in all outputs.
This post is full-time and fixed term for 2 years
The post is only available as full-time on-site.
About You
To be considered for this position you should;
- Hold a BSc in a relevant quantitative discipline (e.g. bioinformatics, statistics, data science, computational biology, mathematics, or equivalent experience)
- Demonstrable experience performing independent data analysis on complex datasets in a research or clinical environment
- Strong applied knowledge of statistical and analytical methods, including appropriate selection, implementation, and interpretation of analyses
- Experience working with large and complex biological and/or clinical datasets, including handling missing data, data cleaning, and quality control
- Proficiency in at least one programming language used for data analysis (e.g. R or Python) and experience working with reproducible analytical workflows
- Ability to independently produce high-quality analytical outputs (e.g. figures, tables, summary statistics, and datasets suitable for publication)
- Understanding of good data governance practices, including secure handling of sensitive human data and adherence to relevant ethical and research governance requirements
- Ability to interpret research questions and translate them into appropriate analytical approaches in collaboration with investigators and statisticians
About the Radcliffe Department of Medicine (RDM)
The Radcliffe Department of Medicine (RDM) within the Medical Sciences Division is one of the largest departments in the University of Oxford. RDM is a multi-disciplinary department which aims to tackle some of the world’s biggest health challenges by integrating innovative basic biology with cutting edge clinical research.
Benefits of working
As an employer, we genuinely care about our employees’ wellbeing and this is reflected in the range of benefits that we offer including:
- An excellent contributory pension scheme
- A comprehensive range of childcare services
- Discounted bus travel and Season Ticket travel loans
- Membership to a variety of social and sports clubs
Application Process
Applications for this vacancy are to be made online. You will be required to upload a supporting statement and CV as part of your online application. The supporting statement should include a cover letter and should also clearly describe how you meet each of the selection criteria listed in the job description. Click here for more information and advice on how to write an effective supporting statement.
Only applications received before 12:00 midday on 08/07/ 2026 will be considered. Interviews will be held as soon as possible thereafter.
