Data Scientist, Machine Learning

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VALCE Talent Solutions

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2016

🤝 B2B

🎯 Recruiter

💼 Consulting

B2B • Recruitment • Consulting

VALCE Talent Solutions is a company specializing in nearshoring, IT talent acquisition, and consultancy aimed at helping businesses expand globally, particularly in Mexico, LATAM, and the United States. They offer customized solutions that include talent recruitment, workforce management, and the integration of technology and artificial intelligence to enhance business processes. With a strong focus on strategic consulting, VALCE aims to connect technology, talent, and tangible results, boasting a robust network of IT professionals and a proven track record of successful placements and project scaling.

📋 Description

• 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.

🎯 Requirements

• 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)

🏖️ Benefits

• Health insurance • Professional development