Senior Data Scientist - Geospatial Foundation Models

About SatSure

SatSure 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.

Role

In 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 ResponsibilitiesData 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