Location
Office/lab based at the UK Health Security Agency Scientific Campus in Colindale, London Borough of Barnet.

Hours
Full Time (hours not specified)

Salary
£46,310 - £52,113 (Senior Executive Officer grade, Inner London)

About the Role
Data science is a broad and fast-moving field spanning maths, statistics, software engineering and communications. Data scientists often work as part of multidisciplinary teams, using data and analytics to inform and achieve organisational goals.

In this senior role within the Emerging Infection and Zoonoses team, part of the Travel, Acute Respiratory, Zoonoses and Emerging Infections and Tuberculosis (TARZET) Division of the UKHSA Clinical and Public Health Group, you will work independently to develop complex data science solutions. You will explore and visualise data, make recommendations to address complex problems, and inform strategic and operational decision making.

You will use a range of statistical tools and techniques, including machine learning and predictive analytics, to find patterns in data and transform them into organisational insight. Ethical use of data, innovation, adaptability, and collaboration with scientific, clinical, and analytical teams to deliver secure, scalable products are key aspects of this role.

UKHSA is committed to being an inclusive employer where everyone matters, promoting equality of opportunity and fostering a culture where differences drive innovative solutions to meet the needs of our workforce and wider communities.

Experience
- Strong proficiency in applying analytical and statistical techniques across real-world codebases.
- Practitioner level skills in SQL and Python and/or R with fluency in Git-based workflows including branching, code review, testing, and reproducible environments.
- Experience in wrangling, cleaning, and preprocessing large and complex datasets.
- Applied understanding of machine learning approaches such as regression, classification, clustering, and appropriate validation strategies.
- Ability to work independently and collaboratively on multiple concurrent projects, managing scope, risks, and dependencies.
- Highly proficient organisational skills with the ability to prioritise and meet tight deadlines.
- Ability to produce clear technical and non-technical documentation and maintain accurate project records.
- Ability to construct and communicate persuasive data stories using fit-for-purpose visualisation with clear narrative, including communicating uncertainty and limitations.
- Solid understanding of quality assurance, data validity, reliability, confidentiality, and statutory information governance requirements.
- Well-developed problem-solving skills and a flexible approach to solution development.
- Excellent interpersonal skills with the ability to influence and communicate effectively with technical and non-technical stakeholders.
- Ability to make sound, evidence-based decisions and escalate appropriately with well-reasoned options.

About You
- Inquisitive, innovative, adaptable, and ethical in your use of data.
- Able to explore and visualise data to inform strategic and operational decision making.
- Collaborative team player who supports capability building within the organisation.
- Able to prepare, manipulate data, perform complex analytics, and present findings effectively.
- Committed to inclusion, diversity, and equality in the workplace.

Qualifications
- A first- or second-class honours degree in a numerate discipline, computer science, or IT equivalent demonstrating core statistical skills.
OR
- Relevant experience working in a statistical/data science field with continuous professional development at foundation degree/HND (Level 5) level.

Desirable
- Understanding of public health and healthcare intelligence, including surveillance, needs assessment, audit, and commissioning support.
- Experience using clinical, epidemiological, environmental, and socio-economic data for public health incident response and research.
- Strong understanding of epidemiology, including transmission dynamics, surveillance methods, sampling design, bias, and confounding.
- Experience or interest in bioinformatics, pathogen genomics, and molecular epidemiology.
- Experience producing analytical products such as software tools, automated reports, dashboards, and visualisations.
- Experience applying generative AI techniques, including large language models (LLMs) and retrieval-augmented generation (RAG).