Senior Geospatial Data Scientist

1w1 week ago

Syngenta Group

Madrid, ES · Full-time · €65,000 – €95,000

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. Collaborate with agronomists, data scientists, and software engineers. Translate complex geospatial insights into practical recommendations for agricultural management.

Maintain technical leadership by staying current with advancements in geospatial technologies and machine learning. Contribute to technical reports, scientific publications, and presentations. Pioneer innovative approaches for feature extraction from remote sensing data to enhance model performance.

Requirements

Advanced degree (Master's or PhD) in Geographic Information Science, Remote Sensing, Computer Science, Data Science, or related field with geospatial focus
5+ years of experience in geospatial data analysis and machine learning, preferably in agricultural or environmental contexts
Strong Python programming skills with expertise in geospatial libraries (GeoPandas, Rasterio)
Demonstrated proficiency in applying machine learning and deep learning techniques to geospatial problems
Expert knowledge of GIS software (ArcGIS, QGIS) and geospatial databases
Proven experience working with diverse geospatial data formats including satellite imagery, LiDAR, and vector data
Experience with cloud-based geospatial processing platforms (Google Earth Engine, AWS, Azure)

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