Data Scientist

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 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, bereavement leaves

Interview Process:

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