Geospatial Data Scientist

About EarthByte Lab

EarthByte Lab delivers innovative geospatial solutions, empowering organizations to make data-driven decisions. We specialize in GIS, remote sensing, and advanced spatial data analysis, providing actionable insights for industries such as environmental monitoring, agriculture, and land management. We are committed to advancing geospatial technology to solve real-world problems and welcome collaboration for tailored solutions.

Role Description

We are seeking a Geospatial Data Scientist to collect, process, and analyze spatial datasets to drive actionable insights. You will work with satellite data, develop algorithms and machine learning models, perform spatial analyses, and implement cloud-based geospatial workflows. Collaboration with cross-functional teams to design and deploy geospatial solutions is a key part of the role.

Contract Duration: 6 months

Location: Remote (US-based candidates only)

Work Authorization: Must be authorized to work in the United States

Key Responsibilities

• Analyze and process satellite imagery (Sentinel-1/2, Landsat, Planet, MODIS, VIIRS, etc.)
• Develop geospatial models (vegetation indices, LULC, wildfire, drought, water stress, soil moisture, etc.)
• Implement machine learning and geospatial AI workflows (classification, segmentation, predictive modeling)
• Build scalable data pipelines using Python, Google Earth Engine, and cloud platforms
• Integrate multi-source spatial datasets (LiDAR, SAR, climate, sensor data)
• Develop reproducible scripts, notebooks, dashboards, and data products
• Produce reports, maps, and insight summaries for clients
• Collaborate on R&D, prototyping, and new solution development

Qualifications

Required

• Bachelor's or Master's in GIS, Remote Sensing, Geospatial Science, Environmental Science, Computer Science, or related field
• Strong Python skills and experience with geospatial libraries (GDAL, Rasterio, GeoPandas, Xarray)
• Experience with satellite imagery and remote sensing analysis (Sentinel, Landsat, Planet, MODIS, VIIRS)
• Familiarity with Google Earth Engine or other cloud geospatial platforms
• Understanding of machine learning concepts and applied modeling
• Ability to work independently and communicate technical results clearly
• Authorized to work in the United States

Preferred

• Advanced Python (NumPy, Pandas, SciPy, scikit-learn, PyTorch/TensorFlow)
• Geospatial libraries: GDAL, Rasterio, GeoPandas, Xarray, rioxarray
• Remote sensing analysis (spectral indices, atmospheric correction, preprocessing)
• Cloud platforms experience (AWS, GCP, Azure)
• Ability to communicate technical concepts to non-technical audiences

Nice-to-Have Skills

• SAR analysis (Sentinel-1, UAVSAR)
• Time-series forecasting and environmental modeling
• LULC classification and segmentation
• Dashboard or web app development (Streamlit, Dash, Leaflet, Mapbox)
• Experience with agricultural, climate, or environmental data
• UX/UI experience for geospatial applications