Senior Data Scientist
| Dyddiad hysbysebu: | 17 Chwefror 2026 |
|---|---|
| Cyflog: | £42,665 i £50,495 bob blwyddyn |
| Gwybodaeth ychwanegol am y cyflog: | National - £42,665 - £46,765 London - £46,060 - £50,495 For details of our pay on appointment policy, please see below under the heading "salary" |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 09 Mawrth 2026 |
| Lleoliad: | Newcastle-upon-Tyne |
| Cwmni: | Government Recruitment Service |
| Math o swydd: | Parhaol |
| Cyfeirnod swydd: | 449148/3 |
Crynodeb
Role 1: Senior Data Scientist (Modelling & Analytics) – contributing to the wider ELM Modelling Strategy, developing analytical software, modelling tools, and data science workflows that support policy and delivery.
Role 2: Senior Data Scientist (Geospatial) – leading the geospatial components of the Digital Environmental Farming Twin (DEFT), FCP’s flagship spatial evidence product.
Both roles offer the opportunity to work at the forefront of environmental and agricultural evidence, collaborating with multidisciplinary teams to deliver high‑impact analytical products.
Role 1: Senior Data Scientist (Modelling & Analytics)
You will contribute to the ELM Modelling Strategy, developing analytical software, modelling tools, and data science workflows that support policy development, operational planning, and strategic decision‑making. You will help build and maintain the core modelling platform used across the programme.
Key Responsibilities
- Develop Analytical Software & Modelling Tools - Contribute to the development of Python modules and modelling components. Support deployment and maintenance of the modelling platform.
- Work with the Strategic Modelling Partner to integrate new modelling capabilities - Build and Improve ELM Models. Develop models of ELM scheme impacts, behaviours, and outcomes. Use data science methods to generate predictions and scenario analyses. Ensure models are robust, well‑documented, and decision‑
- Evidence for Policy & Operations - Work with policy colleagues to understand analytical needs. Apply models to answer specific policy questions. Produce clear, accessible analysis for senior stakeholders.
- Collaboration & Agile Delivery - Lead sprint planning and development using Kanban or similar tools. Support colleagues in using the modelling strategy and analytical tools. Conduct QA of work produced by team members and external partners.
- Capability Development & Best Practice - Promote software engineering best practice for data science. Support other analysts to develop capabilities. Use Git/GitHub for version control, peer review, and reproducibility. Contribute to improvements in tools, processes, and documentation.
Role 2: Senior Data Scientist (Geospatial)
You will lead geospatial analytics, spatial modelling, and data visualisation that support ELM policy and delivery. You will play a central role in developing the DEFT analytical dashboard and expanding FCP’s geospatial evidence products.
Key Responsibilities
- Lead Geospatial Components of DEFT - Develop spatial indicators, parcel‑level metrics, and geospatial datasets. Refine dashboard requirements with policy and modelling teams. Maintain and enhance reproducible spatial pipelines.
- Geospatial Data Science & Environmental Analytics - Lead geospatial modelling and remote sensing workflows using Python, GeoPandas, PySpark/Sedona, and Databricks. Work with national datasets including Earth Observation, OS data, land parcels, and environmental layers. Produce spatial evidence for baselining, land‑use assessment, and scenario modelling.
- Geospatial Visualisation Tools - Build interactive web maps, dashboards, and spatial tools. Use ArcGIS Online, Experience Builder, or similar platforms. Translate complex spatial outputs into intuitive visual formats.
- Evidence for Policy & Operations - Produce clear, robust spatial analysis for ELM design, targeting, and monitoring. Communicate findings to senior stakeholders and non‑technical audiences. Contribute to reports, GOV.UK content, and briefings.
- Collaboration & Best Practice - Work across policy, modelling, and operational teams. Support other analysts and promote geospatial coding best practice. Use Git/GitHub for version control and QA. Contribute to improvements in spatial data standards and workflows.
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