Data Scientist Job at Higher Executive Officer (HEO)

Data Scientist Job at Higher Executive Officer (HEO) – The Data Science QA Team is a small, friendly team who are experts in supporting teams to embed Analytical Quality Assurance (AQA) practices across a wide range of analytical work including data science and modelling.

We are looking for a Data Scientist to support our work maturing AQA across UKHSA. They will provide practical operational support for both the G7 Data Science QA Lead and G7 Modelling QA Lead to ensure analysts across UKHSA understand the importance of AQA, and to provide them with the practical tools to implement proportionate AQA in their day-to-day work.  

Main duties of the job

To support AQA reviews across the analysis spectrum 

To support our work embedding AQA best practice processes and procedures

To help identify and implement opportunities to mature AQA across UKHSA  

To support capability building initiatives to manage analytical risk by ensuring civil servants across UKHSA are aware of AQA and data science best practices and can use them 

At this role level, you will:

• Work independently and collaboratively to support data science and analytical QA. 
• Have a good understanding of a range of topics, including data science techniques, delivery methods and stages, tools and technologies.
• Understand ethical considerations
• Understand the role and benefits of data science within the organisation
• Support data science and AQA capability building within the organisation
• Prepare and manipulate data, and perform complex analytics
• Present and communicate effectively

Detailed job description and main responsibilities

• Applied maths, statistics and scientific practices: You can apply analytical methods including exploratory data analysis, visualisation and statistical testing to reach accurate conclusions. You can use analytical approaches to interpret data confidently. You can summarise and describe data to support decision making.
• Data engineering and manipulation: You can select and use the most appropriate tools and techniques to manipulate and transform the data for data science products. You can collaborate with data engineers.
• Data science innovation: You can apply data science innovation in your approach to new questions, opportunities, data and techniques by using an inquisitive mind. You can improve methods and maximise insights. You can apply your data science innovation to produce creative solutions to data science problems.
• Delivering Business Impact: You can understand processes, data and strategic priorities. You can apply data science techniques to produce and present data science products. You can design solutions that meet user needs, allowing for design and ethics standards. You can work towards the successful delivery of products and establish maintenance requirements.
• Developing data science capability: You can work with data scientists and developers to share skills. You can ensure that your team can access the right technologies to create products. You can develop your data science skills. You can routinely engage with and update your continuous professional development (CPD).
• Ethics and privacy (data science): You can understand and comply with frameworks, standards and laws, including General Data Protection Regulation (GDPR), the Data Protection Act (DPA) and the Equalities Act. You can work with legal, policy and public colleagues, government departments and ministers to clearly communicate ethical issues and ideas. You can store and treat data appropriately.
• Programming and build (data science): You can use programming tools to undertake analysis and create scalable data products. You can identify and reuse common approaches and code. You can document and test code to ensure its quality. You can understand the organisation’s tech stack and get code into production by working with developers and architects.
• Understanding product delivery: You can use different product delivery methods (such as Agile) and phases to make decisions. You can work collaboratively with other professionals. You can use knowledge of product management to deploy data science products into the organisation. You can show data science is used effectively to support products and services.

Essential Criteria

• A first- or second-class honours degree in a numerate discipline, computer science or IT equivalent which demonstrates core statistical skills OR have worked in a statistical/data science field and are able to demonstrate continuous professional development in statistics/data science at the same level as a foundation degree/HND (Level 5)
• Proficiency in Python or R from coursework or projects, including using core libraries (e.g. pandas, numpy, dplyr, ggplot). Can undertake exploratory data analysis and apply statistical techniques and models using a script. Familiarity with Git and version control. 
• Experience in wrangling, cleaning and preprocessing data in readiness for analysis.    
• Understanding of Machine Learning approaches including regression, classification and clustering, using train-test approaches and evaluating performance with metrics such as precision, recall and F1 with guidance.
• Able to manage own tasks to a project plan, collaborate with others and ask for help at the right time. 
• Good organisational skills including planning work, keeping clear notes and meeting deadlines. Can prioritise tasks with support when workloads change. 
• Produces clear charts and dashboards and can explain findings in plain language for both technical and non-technical audiences. Ability to understand caveats and limitations.
• Understanding of quality assurance, data validity, reliability and confidentiality issues and statutory information governance requirements.
• Breaks problems into steps, experiments with simple prototypes. Helps translate user requirements into actionable tasks with guidance.
• Comfortable sharing progress, capturing requirements and incorporating peer and stakeholder feedback.
• Makes day-to-day decisions on own work, documents assumptions and escalates appropriately.

Desirable Criteria:

• Understanding of techniques and applications of public health and healthcare intelligence including surveillance, needs assessment, audit and information support to commissioning.
• Familiarity with a range of health data sources, including clinical, epidemiological, environmental and socio-economic datasets.
• Experience of producing analytical products (e.g., software packages, automated reports, dynamic visualisations/dashboards).
• Familiarity with AI concepts and tools.

Closing Date: 14/11/2025