Data Science Machine Learning

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We are currently partnering with a global IT powerhouse that represents the connected world through innovative customer-centric experiences. As a USD 6 billion organization and one of the top 7 IT service providers globally our client empowers over 1200 global customersincluding several Fortune 500 companiesto Rise. With a massive network of 163000 professionals across 90 countries they are at the absolute forefront of digital transformation leveraging next-generation technologies such as 5G AI Blockchain and Quantum Computing.

This is your chance to thrive in a workplace recognized as one of the most sustainable corporations in the world. You will join an environment that values innovation and societal impact working on end-to-end digital transformation projects for global leaders. If you are a driven professional looking for global career opportunities and exposure to high-impact projects within an international network of expertise this is where you belong.

We are currently searching for a Data Scientist / Machine Learning Engineer:

The Challenge (Responsibilities)

• 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.

Your Profile (Requirements)

• 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.

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.

Languages

• Advanced Oral English: For seamless collaboration with global teams.
• Advanced Spanish.

Special Notes

• Preference for candidates with Space Tech experience though not mandatory.

Work Arrangement

We value flexibility to support your lifestyle. This position is available as:

• Remote.

If you meet these qualifications and are pursuing new challenges start your application on our website to join an award-winning employer. Explore all our job openings Sequoia Careers Page:
Python PyTorch Geospatial SAR Remote Sensing

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).