Data Science / Machine Learning

Description

At Sequoia Connect , we are a Talent-First Technology Ecosystem that redefines how elite professionals interact with the global digital landscape. We move beyond traditional models to act as a catalyst for the top 1% of global talent, connecting human potential with complex industrial execution. By joining our inner circle, you are not simply taking a position; you are aligning with a strategic partner dedicated to updating your "Human OS" and accelerating your growth through world-class, high-impact projects.

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 1,200 global customers—including several Fortune 500 companies—to "Rise™." With a massive network of 163,000+ 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).

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High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery.

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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)