Job Profile: Data Science / Machine Learning

Job Type: Full-Time Nomina based job Opportunity

Location: 100% Remote in Mexico

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"""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."""

📌 Data Science / Machine Learning/ 100% Remote in Mexico (México)
🏢 Pyramid Consulting
📍 México