Food Standards Scotland (FSS) is the public sector food body for Scotland. We work independently of Government and Ministers to deliver a safe, healthy, and sustainable food environment that benefits and protects the health and wellbeing of everyone in Scotland. FSS ensures that information and advice on food safety and standards, nutrition and labelling is independent, consistent, evidence-based, and consumer-focused.

FSS’s Science Division is responsible for providing scientific advice, commissioning research and surveillance to support the development of policies aimed at protecting consumers in Scotland from food and animal feed safety risks. The Division also provides data science and social research expertise to ensure strategies for improving dietary health and protecting the food chain is informed by robust evidence. The Science Division provides a support function for all areas of FSS business. It engages with public health nutritionists and policy, veterinary, environmental health, and communications professionals across the organisation as well as external stakeholders to help FSS deliver robust, evidence-based interventions which achieve long lasting benefits for the people of Scotland.

You will be based in the Analytical Unit of FSS’s Science Division, working alongside a senior data scientist, social researchers, and an economist as part of a wider team of food safety scientists. The key purpose of the role is to develop FSS’s evidence bases and underpin our understanding of risks to the food chain, enabling us to identify and implement interventions for improving the Scottish diet.

You will be managed by the senior data scientist but will be required to work autonomously, with minimal supervision, engaging across all FSS’s business areas to identify evidence needs and provide advice on data analysis, statistics and data sources needed to support policy development. As the analytical team in FSS is relatively newly established, it works collaboratively with the Scottish Government’s Marine Analytical Unit to ensure sufficient analytical oversight and to support learning and development.

The role will provide an opportunity to work across the whole of the project lifecycle. In addition to traditional statistical analysis, it will use data science techniques (data visualisation, machine learning algorithms and data linkage) to support FSS in delivering its statutory responsibilities to identify and monitor risks to the food chain, assess the performance of food and feed enforcement authorities (such as the interventions local authorities carry out at food businesses) and provide evidence based advice on how to best implement more efficient or sustainable approaches to delivering official controls.

You will have prior experience in a relevant research field and/or the analysis and interpretation of data and evidence to inform recommendations or decisions. Specific experience of applying statistics, data science and/or analytical support relevant to public health, food, veterinary or agriculture disciplines would be an advantage but is not essential.

Analysis and Use of Evidence

Communication and Engagement

Improving Performance

Self-Awareness

1. Practical experience in data analysis and data management, including proficient use of at least one of the following programming languages such as R, Python, SQL to deliver high quality code according to best practice methodologies (version control, test-driven development)
2. Proven analytical skills, demonstrating accuracy and attention to detail, with an strong understanding and experience of using a wide range of data science and statistical methods (i.e. regression analysis, significance testing, cluster analysis, predictive modelling techniques such as supervised and unsupervised machine learning, natural language processing, data visualisation) to draw conclusions from multiple data sources to support decisions and make recommendations on policy development or business planning and delivery.
3. Excellent communication skills with experience of explaining complex analytical techniques and insights (data stories) to colleagues and stakeholders of all levels, who may not share your technical background, in a clear and concise manner.
4. A clear track record of building successful working relationships across different business areas or organisations with an ability to balance competing demands, quickly learn new analytical techniques, contribute ideas, share best practice and embrace new ways of doing things to solve challenging problems and deploy high-quality robust solutions to meet business needs and deadlines in order to improve the performance of a service.

Qualifications
No specific qualifications required for this post.

Provisional Interview Dates
To be confirmed.

The role is hybrid working mainly from home and can also be based in Aberdeen, Glasgow, or Edinburgh.

Some travel within the UK may be required depending on the nature of the work e.g., engagement with stakeholders and attendance at conferences and project meetings.

The post is full time based on 37 hours per week with flexible working hours in a band between 07:00 – 19:00 (Monday to Friday)

For further information on this vacancy please download and review the “Person Specification” which you will find below.

Person Specification Band B

To apply, you must fully complete and submit an online application via this website before the closing date using evidence based examples to demonstrate your knowledge/skills (300 words). Remember, your answers should be clear, concise and reflect what actions you undertook. You may want to use the STAR(R) approach to respond to each criterion.

To learn more about this opportunity, please contact Rebecca Callaby at [email protected]