Geospatial Data Scientist

About the Role We are looking for a Geospatial Data Scientist to turn complex spatial data into actionable insights. You will work across the full geospatial pipeline — from data ingestion and processing to analysis, modeling, and visualization — and collaborate closely with engineering and product teams to embed spatial intelligence into our platform. 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