Geospatial Data Scientist

JR United Kingdom; London; Hybrid; GBP 40,000 - 70,000; Full time

Job summary

A leading company in agricultural systems improvement seeks a versatile Geospatial Machine Learning Scientist or Applied Statistician in London. The role involves developing classification models, analyzing complex datasets, and collaborating in a fast-paced environment. Ideal candidates will have a strong quantitative background and experience with geospatial imagery and computer vision techniques.

Qualifications

• Degree in mathematics/statistics, data science, remote sensing, or computer science required.
• Experience with geospatial imagery and computer vision techniques essential.
• Strong statistical background and communication skills needed.

Responsibilities

• Design and implement classification models for land cover detection.
• Analyze datasets from diverse sources and communicate findings.
• Collaborate in small teams to develop models.

Skills

Geospatial imagery

Computer vision techniques

Classification algorithms

Statistical analysis

Communication skills

Education

Degree in a quantitative field

Tools

Google Earth Engine

GeoPandas

GDAL

Job description

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Location:

london, United Kingdom

Job Category:

Other

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EU work permit required:

Yes

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Job Views:

1

Posted:

23.05.2025

Expiry Date:

07.07.2025

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Job Description:

• Location: London, UK (Hybrid, minimum 3 days a week in office)
• Hours: Full Time
• Contract type: Permanent

Company

At Food Systems Innovation & Delivery (FSID), we combine cutting-edge predictive modelling with industry-grade software development to support the improvement of agricultural systems in both the developing and developed world.

A key pillar of our work focuses on predictive modelling to improve the early detection, surveillance and management of pest and disease outbreaks impacting crops that currently cause up to 40% of yield loss globally.

Objective of the role

We’re looking for a versatile and collaborative Geospatial Machine Learning Scientist or Applied Statistician with a deep interest in remote sensing, computer vision and classification problems. You’ll play a core role in developing models, exploring complex datasets, and communicating results that directly influence product and strategy. This position is ideal for someone who enjoys wearing multiple hats and is excited to work in a fast paced, early-stage environment.

Key Responsibilities

• Design and implement classification and computer vision models to detect, classify, and segment land cover types and change detection from remote sensing imagery.
• Work with multi-resolution and multi-temporal geospatial imagery (e.g., Sentinel, Landsat, commercial satellites).
• Design and implement machine learning and statistical models that handle spatio-temporal data.
• Analyse datasets (both large and small) from diverse sources (e.g., sensor networks, geospatial APIs, remote sensing).
• Communicate findings clearly and regularly to both technical and non-technical team members.
• Prototype/experiment with algorithms in multiple languages; while Python is ideal, openness to others (e.g., C++) is beneficial.
• Collaborate in small teams.

Skills and Qualifications

Required:

• Degree in a quantitative field - such as mathematics/statistics, data science, remote sensing, computer vision, computer science.
• Demonstrated experience with geospatial imagery, computer vision techniques and classification algorithms (e.g., semantic segmentation, object detection, pixel classification, random forest, convolutional neural networks).
• Experience with cloud platforms or geospatial pipelines (e.g., Google Earth Engine).
• Strong statistical background.
• Familiarity with fast and critical review of scientific literature.
• Great communication skills in explaining complex ideas to a range of audiences.
• Willingness to explore unfamiliar tools and languages as needed.

Desirable but not required:

• Experience with geospatial libraries (e.g., GeoPandas, GDAL).
• Demonstrated experience in geostatistical modelling techniques.
• Demonstrated experience working with spatio-temporal datasets and methods (e.g., kriging, spatial autocorrelation, trajectory analysis, or related fields).
• Experience with weather and climate data
• Familiarity with data pipelines, MLOps, or cloud computing.
• Contributions to open-source or academic publications
• Masters or PhD

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.

To apply for this position, please email your CV and supporting statement to [emailprotected]