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 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
Preferred BackgroundDomain Expertise• 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.)
Data Engineering at Scale• 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
Tooling (Geo Stack)• Experience with geospatial data tooling, such as:• Xarray, Dask, Rasterio, Zarr
• Google Earth Engine (nice to have)

Mindset• 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
Benefits:• Medical Health Cover for you and your family, including unlimited online doctor consultations
• Access to mental health experts for you and your family
• Dedicated allowances for learning and skill development
• Comprehensive leave policy with casual leaves, paid leaves, marriage leaves, and bereavement leaves
Interview Process:• Intro call
• Assessment
• Presentation
• Interview rounds (ideally up to 3-4 rounds)
• Culture Round / HR round

Department: Dhaarini
Experience: 3-5
Posted: 2026-06-19T06:01:57.307Z