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