Data Scientist – Geospatial Foundation Models

About SatSureSatSure is a deep tech, decision intelligence company working at the nexus of agriculture, infrastructure, and climate action — creating impact for the other millions, with a focus on the developing world. As part of this mission, we're building geospatial foundation models that learn directly from Earth observation data — optical, SAR, and elevation — at scale. This role sits at the heart of that effort: architecting and training large-scale models that can generalize across geographies, sensors, and time. You'll be shaping the core intelligence layer that powers insights for millions, not just fine-tuning someone else's model.
RoleIn foundation model development, data is the moat. You will drive the transformation of petabytes of raw geospatial data into a high-quality, high-entropy training and evaluation corpus.This role sits at the intersection of remote sensing, data engineering, and ML, ensuring that models learn from diverse, representative, and well-curated data at scale.
Key Responsibilities****Data Curation & Pre‐training Datasets

• Design and implement data curation pipelines for large‐scale pre‐training datasets
• Develop sampling strategies to ensure:
  • Geographic and biome diversity
  • Coverage across seasons, sensors, and resolutions
• Mitigate dataset biases (e.g., over‐representation of cloud‐free or high‐income regions)
• Balance trade‐offs between data quality, diversity, and scale

Evaluation Frameworks (Earth‐Bench)

• Design and own a comprehensive evaluation framework ("Earth‐Bench") to assess:
  • Representation quality (post‐SSL embeddings)
  • Transfer performance on downstream tasks:
    • Segmentation
    • Yield prediction
    • Disaster mapping
• Define metrics and benchmarks that reflect real‐world generalization across geographies and time
• Continuously evolve evaluation as new datasets, sensors, and tasks emerge

Data Systems & Pipeline Thinking

• Build and maintain scalable data pipelines for ingestion, processing, versioning, and access
• Work with ML and platform teams to:
  • Enable efficient data loading and training at scale
  • Optimize storage formats and access patterns (e.g., chunking, caching)
• Ensure datasets are:
  • Reproducible
  • Well‐documented
  • Easily usable across teams

Data‐Centric ML Thinking

• Analyze how data quality, diversity, and freshness impact model performance
• Partner with researchers to:
  • Identify failure modes driven by data gaps
  • Improve datasets to unlock model gains (not just model changes)
• Treat data as a first‐class lever for improving model quality

• 3–5 years of experience in Applied Data Science at scale
• Strong understanding of remote sensing fundamentals, including:
  • Atmospheric correction
  • SAR backscatter
  • Orthorectification
• Familiarity with multi-sensor data (optical, SAR, DEM, etc.)
• Experience working with large-scale (TB–PB) datasets across the ML lifecycle
• Hands-on experience with:
  • Distributed data processing
  • Efficient storage and retrieval strategies
• Understanding of how data pipelines interact with model training workflows
• Experience with geospatial data tooling, such as:
  • Xarray, Dask, Rasterio, Zarr
  • Google Earth Engine (nice to have)
• Strong data intuition—ability to reason about bias, coverage, and representativeness
• Systems thinking: understands how data decisions impact model behavior at scale
• Comfortable working in ambiguous, evolving problem spaces