Geospatial Data Scientist / Modeller - Fixed term
- Location: London, UK (minimum 4 days/week in office)
- Hours: Full Time
- Start date: Immediately available
- Contract type: Fixed term (until December 2026)
- Salary: £40-50k
Company
At Outbreak Labs, we develop predictive models to improve the early detection, surveillance and management of pests and diseases that cause agricultural yield losses between 20-40% globally.
Objective of the role
We’re looking for a versatile and collaborative Geospatial Data Scientist / Modeller with experience in data coordination, literature synthesis and machine learning models. You’ll play a core role in managing the data collection from a large project with complex data sources, reviewing literature in key areas and the development of earth observation models. This position is ideal for someone who enjoys wearing multiple hats and is excited to work in a fast paced, early-stage start up environment.
Key Responsibilities
- Support the coordination and organisation of ground truth data collection for earth observation ML model development, working closely with remote teams responsible for data acquisition
- Conduct literature reviews on pest and disease identification and the use of earth observation, and develop reference databases covering pests and diseases more broadly
- Work with and adapt Python code across machine learning models and potential disease modelling approaches
- Collaborate closely with team members, including infectious disease modellers and software engineers
- Review scientific literature and engage with partner organisations to identify and curate datasets
- Carry out manual validation of Earth observation datasets
Skills and Qualifications
Required
- Master’s or PhD in a relevant subject area
- Strong understanding of data collection processes, including awareness of potential sources of bias, data quality issues, and common failure modes in real-world datasets
- Strong literature review and synthesis skills
- Good communication skills, with ability to work effectively across technical and non-technical teams
- Experience working with and curating complex datasets, with persistence in sourcing hard-to-find data
- Solid understanding of machine learning, particularly classification models
- Proficiency in Python (or a similar programming language, with a willingness to work extensively in Python) and experience working with existing codebases
- Comfortable working flexibly and engaging with a wide range of tasks
Desirable (not required)
- Experience with Earth observation or remote sensing data
- Experience in agricultural systems, pests, or plant pathology
- Familiarity with spatial data analysis (e.g. GIS, geospatial datasets)
- Exposure to epidemiological, ecological, or disease modelling approaches
- Experience using version control systems such as Git
- Understanding of data workflows or basic data engineering practices
Apply
Please provide your CV and a short supporting statement (approx. Half a page) summarising why you are suited to the role, and how you meet the selection criteria.