Farming Statistics Analysts

Defra Farming Statistics teams are responsible for producing key departmental statistics on all aspects of the farming sector to monitor the structure, size and value of the industry. We are also responsible for monitoring farmer incomes, profitability, land use, activities of the agricultural industry and volumes of domestic food production. Our data are used extensively across the department and wider industry for monitoring the English and UK farming sectors, for UK commodity market monitoring and for international agricultural data reporting. The data are also heavily used in secondary analyses such as calculating greenhouse gas emissions from agriculture, fertiliser use, and soil nutrient run-off for policy development and monitoring. All our publications are available to view at https://www.gov.uk/government/collections/food-farming-and-bio-security-statistics The team consists of approx. 30 data analysts and statisticians and we currently have a number of analyst vacancies across the team. The roles can be carried out from any of the listed Defra policy hub locations, with an expectation of occasional travel to the other hubs for team meetings. Your specific areas of responsibility will depend on priorities at the time of recruitment along with the your interests and experience. Examples of the type of work you could be involved in include:

• Running farm-based surveys to estimate the size, structure and incomes of the farming sector.
• Running monthly primary processor surveys to monitor volumes of food production (milk, meat and eggs).
• Running an annual inventory to monitor pedigree livestock breed populations.
• Valuing the economic contribution, productivity and profitability of the farming sector.
• Producing bespoke analyses for policy teams, often to tight deadlines for ministerial briefings or in response to sector emergencies.
• Preparing regular statistical publications for the Defra website or compendium publications, such as "Agriculture in the UK". All our publications are based on the standards of the UK Statistics Authority Code of Practice for Statistics, maintaining trustworthiness, quality, and value.
• Adopting modern analytical approaches using R (including use of GitHub, Reproducible Analytical Pipelines, process automation, Cloud data storage).
• Developing improved data collection methods, including opportunities to link data from administrative data sources.
  
  Disability Confident
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