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 scaleEvaluation 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 qualityPreferred BackgroundDomain 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 experience with:
• Distributed data processing
• Efficient storage and retrieval strategies

• Understanding of how data pipelines interact with model training workflowsTooling (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 spacesBenefits:

• 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, bereavement leaves

Interview Process:

• Intro call
• Assessment
• Presentation
• Interview rounds (ideally up to 3-4 rounds)
• Culture Round / HR round