[Hiring] Data Scientist / Machine Learning Engineer @Sequoia Connect

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.