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# Senior Geospatial Data Scientist

- **Company:** SOFTWARE.CO TECHNOLOGIES, INC.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Geographic Information Systems, Python (Programming Language), NumPy, SciPy, Data Processing, Model Validation, Information Technology, Dask
- **Published:** October 29, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pvbj19gj8v

## About the Role

* 4-6 years of hands on experience in satellite data processing or remote sensing science.
 * Strong Python based geospatial stack - rasterio, xarray, geopandas, dask, numpy, scipy.
 * Experience with optical and radar satellite imagery processing at regional or national scale.
 * Has built temporal smoothing, gap filling, or change detection pipelines on satellite time series.
 * Has worked across multiple agro climatic zones in India.
 * Experience with building or structure detection from satellite imagery is a strong advantage.
 * Familiarity with geospatial foundation models is a strong advantage.
 * Experience with OpenStreetMap data and proximity analysis.
 * Strong model validation skills across diverse geographies.
 * Degree in Geography, Geoinformatics, Remote Sensing, Environmental Science, Computer Science, or related field preferred.
 * Background from ISRO, IIRS Dehradun, IISc, IIT or earth observation focused organisations is a strong advantage.

## Description

* Build and maintain pixel level processing pipelines across multiple satellite and geospatial data sources at national scale.
 * Design and implement temporal science pipelines on satellite time series data.
 * Build and validate classification and change detection models across diverse Indian geographies.
 * Develop localisation frameworks calibrated to different agro climatic zones across India.
 * Build structure level intelligence from satellite imagery.
 * Construct proximity and connectivity intelligence frameworks at village level.
 * Ensure data quality, consistency, and scalability across all geospatial pipelines at production scale.