GeoAI Data Scientist
Pubblicato il 22-08-2026 - XML International in Udine
Senior Geospatial Data Scientist | GeoAI | Earth Observation
Applications welcome from across Italy | On-site in Brindisi
On-site role based in Brindisi. Applications are welcome from candidates across Italy who are willing to relocate or work on-site.
Help Build AI Solutions for Climate Resilience and Environmental Intelligence
We are looking for an experienced Senior Geospatial Data Scientist to develop innovative GeoAI solutions that transform Earth Observation data into actionable insights for environmental monitoring, climate resilience, and natural hazard assessment.
This role combines Artificial Intelligence, Remote Sensing, Cloud Computing, and Geospatial Analytics to build predictive models, large-scale data pipelines, and interactive geospatial solutions supporting high-impact environmental initiatives.
If you're passionate about GeoAI, satellite imagery, machine learning, and solving global environmental challenges, we'd love to hear from you.
What You'll Do
GeoAI & Data Science
- Design and develop machine learning and deep learning models for environmental monitoring and natural hazard assessment.
- Build predictive models for change detection, time-series analysis, and land-use dynamics.
- Develop AI-driven solutions using large-scale Earth Observation datasets.
- Transform complex geospatial data into actionable intelligence.
Earth Observation & Remote Sensing
- Integrate data from Sentinel-2, Landsat, SAR, LiDAR, and other Earth Observation sources.
- Process multispectral and radar imagery for environmental analysis.
- Develop workflows supporting climate resilience and disaster risk management.
Cloud Geospatial Computing
- Design and manage large-scale geospatial data pipelines.
- Work with Google Earth Engine (GEE), Google BigQuery, and cloud-native geospatial environments.
- Build scalable Raster and Vector processing workflows.
GIS & Solution Development
- Develop Web GIS applications and interactive dashboards.
- Create scalable geospatial v