Geospatial Data Scientist

Responsibilities

• Design and execute geospatial analyses to support product and business decision-making
• Build, validate, and maintain spatial data models and pipelines
• Query and manage geospatial datasets using PostgreSQL with PostGIS
• Work with geospatial data formats including GeoJSON, Shapefile, GeoTIFF, WKT, and WKB
• Develop machine learning models with a spatial component (clustering, classification, interpolation, etc.)
• Create maps, dashboards, and visualizations to communicate findings to technical and non-technical stakeholders
• Collaborate backend engineers to integrate geospatial features into production systems
• Evaluate and maintain geospatial data quality, coverage, and accuracy

Requirements

• 3–6 years of experience in data science, GIS, or a related field
• Strong proficiency in Python for data analysis and modeling (GeoPandas, Shapely, Fiona, Rasterio, or similar)
• Deep experience with PostgreSQL and PostGIS for spatial querying and data management
• Familiarity with geospatial standards and formats (GeoJSON, Shapefile, GeoTIFF, WMS/WFS, etc.)
• Experience with GIS tools such as QGIS, ArcGIS, or equivalent
• Solid understanding of coordinate reference systems (CRS), projections, and spatial indexing
• Experience applying machine learning techniques to spatial problems
• Ability to communicate findings clearly in a fully remote, async environment

Nice to Have

• Experience with remote sensing or satellite imagery analysis
• Familiarity with cloud-native geospatial tools (PostGIS on AWS RDS, Google Earth Engine, etc.)
• Exposure to spatial data infrastructure (GeoServer, MapServer, Mapbox, Deck.gl)
• Experience with big geospatial data processing (Apache Sedona, H3, S2)
• Knowledge of Docker and containerized data workflows
• Familiarity with CI/CD and version control best practices