Data Scientist - 2

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:

We're looking for a Data Scientist with strong fundamentals in computer vision and machine learning to work on geospatial problems at a planetary scale. You'll develop models that turn satellite imagery into a structured, actionable understanding of the Earth's surface- work that directly impacts how decisions are made across agriculture, forestry, climate, and beyond.

This is a hands-on IC role. You'll own problems end-to-end: from formulating hypotheses and building datasets to training models, validating them rigorously, and shipping production-ready systems. You'll work with terabytes of multi-temporal, multi-sensor imagery and build solutions that generalise across geographies, seasons, and sensing modalities.

Responsibilities:

• Develop and ship computer vision models for tasks such as semantic segmentation, change detection, super-resolution, and temporal sequence modelling.
• Own the full lifecycle of ML projects: problem scoping, data pipeline design, model development, evaluation, deployment, and monitoring.
• Design experiments rigorously, define meaningful metrics, and calibrate them against business outcomes.
• Write clean, production-grade code in Python/PyTorch. Work with distributed training infrastructure and large-scale data pipelines.
• Collaborate closely with product, MLOps, platform, and geospatial teams to translate ambiguous real-world problems into well-scoped technical solutions.
• Communicate findings clearly through internal documents and presentations.

Must Have

• 3+ years of applied ML/Computer Vision experience, or PhD with 1+ year of post-degree experience, in CS, EE, or a related field.
• Strong fundamentals in semantic/instance segmentation, object detection, and encoder-decoder and transformer architectures.
• Proven experience taking models from prototype to production.
• Proficiency in PyTorch and Python. Familiarity with distributed training and MLOps tooling.

Good to Have

• Experience with generative models (GANs, diffusion), self-supervised and contrastive learning, domain adaptation, semi-supervised learning, or model compression (distillation, pruning, quantisation).