Geospatial Data Scientist

Geospatial Data Scientist


Location :- Remote Job


Duration :- 11 Months


**Experience:** 3-6 years


## 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