Data Science / Machine Learning

Data Science / Machine Learning

We have an urgent requirement for a Data Scientist, preferably with Space Tech experience but not mandatory.# Data Scientist preferably with specialization in Geospatial / Remote Sensing

## About the role We are building a satellite-based early-warning system that detects wind-driven sand encroachment and pipeline displacement across a desert pipeline network, using Sentinel-1 (SAR) and Sentinel-2 (optical) imagery, foundation models, and physical dune-migration modelling. You will own the detection and calibration science: turning raw satellite passes into calibrated, validated alerts against real field-logged events.

What you will do- Build and calibrate change-detection and anomaly models on multi-temporal Sentinel-1/2 imagery over pipeline corridors. - Learn per-site """"normal terrain"""" baselines and validate detections against a ground-truth event log (detection rate, lead time, false-positive rate, AUC). - Fine-tune geospatial foundation models (Prithvi-EO / similar) with LoRA/PEFT on limited labelled data. - Implement SAR techniques for displacement: amplitude change, coherence, and pixel-offset tracking to measure pipe and dune movement. - Develop dune-migration tracking (optical flow / feature tracking), migration direction, and mobility indices. - Engineer robust ingestion from Copernicus (CDSE / Sentinel Hub / STAC) and fuse optical, SAR, DEM, and ERA5 wind data. - Design labelling strategy (encroachment masks, severity) and a train/validation split that avoids leakage. - Communicate results and limitations honestly to technical and business stakeholders.

Required -7+ years applied data science / ML, with hands-on geospatial remote sensing. - Strong Python: numpy, rasterio/GDAL, xarray, scikit-image, geopandas/shapely. - Working knowledge of optical and SAR data (spectral indices, backscatter/dB, resolution trade-offs, revisit). - Deep learning with PyTorch; experience fine-tuning models (transfer learning, LoRA/PEFT). - Model validation and calibration: ROC/AUC, thresholding, cross-validation, handling weak/few labels.- Time-series / change-detection methods and coordinate reference systems (UTM, reprojection).

Nice to have - InSAR / SAR offset tracking (SNAP, ISCE, or equivalent) for surface/structure displacement. - Geospatial foundation models (Prithvi-EO, TerraTorch, HLS) and segmentation. - Copernicus/CDSE, Sentinel Hub, STAC, Planetary Computer. - Aeolian geomorphology / dune dynamics; oil & gas or pipeline-integrity domain exposure. - MLOps and cloud (containerisation, scheduled inference, geospatial data pipelines).

Qualifications - MSc/PhD in Remote Sensing, Geospatial Science, Earth Observation, CS/ML, Physics, or equivalent experience

ADVANCED ENGLISH

REMOTE

Originally posted on Himalayas