Sr. Data Scientist

Geospatial Data Scientist

Remote

This is a Remote role.

Compensation: $80 - $88 per hour

About The Role

Our client is seeking a Geospatial Data Scientist to transform complex spatial data into actionable insights and support product and business decision-making. In this role, you will work across the full geospatial data 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. You will be responsible for developing and maintaining spatial data models, applying machine learning techniques to spatial problems, and creating compelling visualizations for diverse stakeholders. The ideal candidate thrives in a fully remote, asynchronous environment and brings a solid understanding of geospatial standards, coordinate reference systems, and data quality management.

What You'll Do

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 spatial components (clustering, classification, interpolation, etc.)

Create maps, dashboards, and visualizations to communicate findings to technical and non-technical stakeholders

Collaborate with backend engineers to integrate geospatial features into production systems

Evaluate and maintain geospatial data quality, coverage, and accuracy

Apply GIS tools (QGIS, ArcGIS, or equivalent) for spatial analysis and visualization

Ensure clear communication of geospatial insights in a remote, async environment

Maintain familiarity with geospatial standards, coordinate reference systems, and spatial indexing

Contribute to spatial data infrastructure and cloud-native geospatial workflows as needed

What You Bring

3–6 years of experience in data science, GIS, or a related field

Strong proficiency in Python for geospatial 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, WKT, WKB, 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

Experience with remote sensing or satellite imagery analysis (nice to have)

Familiarity with cloud-native geospatial tools (PostGIS on AWS RDS, Google Earth Engine, etc.) (nice to have)

Exposure to spatial data infrastructure (GeoServer, MapServer, Mapbox, Deck.gl) (nice to have)

Experience with big geospatial data processing (Apache Sedona, H3, S2) (nice to have)

Knowledge of Docker and containerized data workflows (nice to have)

Familiarity with CI/CD and version control best practices (nice to have)