Job description
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 Py. Torch ; 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, Terra. Torch, 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