Senior Geospatial Data Scientist

1w1 week ago

Syngenta Group

London, GB · Full-time · £70,000 – £100,000

About this role

Syngenta Group, a global leader in agricultural technology and innovation, employs 60,000 people across more than 100 countries to transform agriculture through tailor-made solutions. The Geospatial Data Scientist leverages advanced geospatial analytics, machine learning, and remote sensing expertise to transform complex agricultural and earth observation data into actionable insights. This role drives innovation in Syngenta's Computational Agronomy Department.

Develop cutting-edge models and algorithms to extract meaningful patterns from diverse spatial datasets including satellite imagery, drone data, and IoT sensors. Design scalable data processing pipelines for cleaning, transforming, and integrating geospatial data sources. Lead statistical analysis and data mining to identify patterns in spatial and temporal agricultural data.

Work within cross-functional teams to bridge technical expertise with agricultural knowledge, creating scalable solutions for modern agriculture challenges. Translate complex geospatial insights into practical recommendations for agricultural management and decision-making. Collaborate effectively with interdisciplinary teams including agronomists, data scientists, and software engineers.

Maintain technical leadership by staying current with advancements in geospatial technologies and machine learning techniques. Contribute to technical reports, scientific publications, and presentations to communicate research findings. Pioneer innovative approaches to enhance model performance and support sustainable farming practices.

Requirements

Master's or PhD in Geographic Information Science, Remote Sensing, Computer Science, Data Science, or a related field with a strong focus on geospatial analysis
Minimum of 5 years of experience in satellite and geospatial data analysis and modelling, preferably in an agricultural or environmental context
Strong proficiency in Python programming, with experience in geospatial libraries such as GeoPandas and Rasterio
Expertise in machine learning and deep learning techniques, particularly as applied to earth observation problems (e.g., image classification, object detection, time series analysis)
Experience with cloud-based geospatial processing and big data technologies (e.g., Google Earth Engine, Spark)
Experience leveraging geospatial foundation models
Experience with version control systems (e.g., Git) and collaboration tools

Responsibilities

Develop and implement advanced geospatial models and machine learning algorithms to extract actionable insights from agricultural datasets including satellite imagery, drone data, and IoT sensors
Design and maintain scalable data processing pipelines for cleaning, transforming, and integrating diverse geospatial data sources
Lead statistical analysis and data mining initiatives to identify meaningful patterns and relationships in spatial and temporal agricultural data
Deliver high-quality, well-documented code for geospatial data processing using Python and relevant libraries
Translate complex geospatial insights into practical recommendations for agricultural management and decision-making
Pioneer innovative approaches for feature extraction from remote sensing data to enhance model performance
Implement cloud-based solutions for large-scale geospatial data processing and analysis
Collaborate effectively with interdisciplinary teams including agronomists, data scientists, and software engineers