Role Description We are currently searching for a Data Scientist / Machine Learning Engineer: - 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 or 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. Qualifications - 7+ years of applied data science / ML experience, with hands-on geospatial remote sensing. - Strong Python programming skills, including numpy, rasterio/GDAL, xarray, scikit-image, and geopandas/shapely. - Working knowledge of optical and SAR data (spectral indices, backscatter/dB, resolution trade-offs, revisit). - Deep learning expertise with PyTorch, including experience fine-tuning models (transfer learning, LoRA/PEFT). - Proven ability in model validation and calibration: ROC/AUC, thresholding, cross-validation, and handling weak/few labels. - Experience with time-series / change-detection methods and coordinate reference systems (UTM, reprojection). - High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery. - Technologist DNA: A deep understanding of the difference between "coding" and "engineering." Requirements - Desired experience with InSAR / SAR offset tracking (SNAP, ISCE, or equivalent) for surface/structure displacement. - Familiarity with geospatial foundation models (Prithvi-EO, TerraTorch, HLS) and segmentation. - Knowledge of Copernicus/CDSE, Sentinel Hub, STAC, and Planetary Computer. - Exposure to Aeolian geomorphology, dune dynamics, or the oil & gas / pipeline-integrity domain. - Experience with MLOps and cloud environments (containerisation, scheduled inference, geospatial data pipelines). - MSc/PhD in Remote Sensing, Geospatial Science, Earth Observation, CS/ML, Physics, or equivalent experience. - Familiarity with cloud-native foundations or AI coding assistants. Benefits - Work Arrangement: We value flexibility to support your lifestyle. This position is available as Remote. Languages - Advanced Oral English: For seamless collaboration with global teams. - Advanced Spanish. Special Notes - Preference for candidates with Space Tech experience, though not mandatory.