UK Health Security Agency (UKHSA) is an executive agency, sponsored by the Department of Health and Social Care (DHSC). At UKHSA, our mission is to protect people’s health by preventing and responding to threats—whether that’s a new pandemic, everyday infections like influenza, or environmental dangers like radiation or extreme weather. We save lives, protect livelihoods, and work with partners across the NHS, care systems, and globally to keep communities safe.
We’re a hub of scientific and operational expertise, tackling health challenges at every level—local, national, and international.
The Chief Data Officer (CDO) group is the analytical powerhouse of UKHSA and plays a key role in making sure data is used safely, legally, and ethically. We support every step of the data journey—from collecting and managing information to analytics and delivering insights that drive real-world action. If you’re passionate about using data to make a difference and want to help solve big health challenges, there’s a place for you here in the CDO group.
Project Outline
UK Health Security Agency (UKHSA) is looking for a data scientist with previous experience in R programming and machine learning (ML) and a strong interest using genomics data for public health. The successful applicant will support a project in collaboration with partners at the University of Glasgow to deploy a pretrained ML model for identifying emerging Avian Influenza (AIV) strains with zoonotic potential – which are able to infect humans – using viral genomic sequence characteristics.
The project will strengthen UKHSA operational zoonotic risk capability, by deploying an ML-based early warning tool for zoonotic pathogen risk detection, feeding into UKHSA AIV risk assessment and situational awareness reporting.
Placement period
Placement start date: August/September 2026 Duration: 3 months, with strong potential for the placement to be extended as a paid opportunity for additional months with a minimum requirement of 20 hours per week.
Outline of duties:
- Genomic Data Collection and Preparation
- Data preprocessing and ML feature extraction
- Code refactoring
- ML model deployment using Kubernetes OpenShift AI
- Stakeholder collaboration
- Effective communication of outputs and technical concepts
Essential Skills
- R Programming
- Ability to write modular code, and follow other best practices
- Understanding of Machine Learning
- Familiarity / knowledge of git/GitHub, version control
- Maths, programming, statistics
- Evidence of collaboration and independent working
- Evidence of self-development and application of learning
Desirable skills
- Familiarity with genomics and utilising genomic data
- AI Awareness
- Experience with OpenShift or High-Performance computing
Funding
This placement will be offered as an unpaid visiting worker. Applicants should seek funding through their funder/research council to complete this placement. UKHSA shall not be responsible for any payment to the Visitor or for any travelling and accommodation expenses incurred, except for necessary travel authorised in advance by UKHSA. The Visitor will be bound by the UKHSA Business Expense policy in respect of any such expenses. UKHSA shall provide the Visitor with appropriate working facilities during the placement, free of charge.
How to apply
Closing date for applications: 31st July 2026. We anticipate a short turnaround, as the team is keen to appoint a candidate as soon as possible. Therefore, we expect to send interview invitations and times by 7 August 2026.
Application format: CV and a 500 (max) cover letter to evidence how you fit the project requirements, skills and what you can bring to this role.
How to submit an application: Send your CV and a cover letter (max 500 words) to the CDO Partnerships Team, UKHSA, [email protected] with the subject line “AIZR ML YB DTP ” placement application”.
Please note this opportunity is being advertised at multiple universities.
Host contact details
Any queries about this opportunity should be addressed to the Host Organisation.
CDO Partnerships Team, UKHSA
[email protected] – please reference the placement you are enquiring about in the subject heading.