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

• 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