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 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 Preferred Background Domain Expertise 5–8 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.) Data Engineering at Scale Experience working with large-scale (TB–PB) datasets across the ML lifecycle Hands-on…