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.

• Take ownership of VALR's data platform and define the roadmap for how it evolves — across the warehouse layer, ingestion pipelines, orchestration, and data transformation strategy. • Develop, maintain, and optimise scalable ELT pipelines to transform raw data into clean, well-documented, analytics-ready datasets. • Implement data contracts, modeling conventions, and documentation standards that enable consistent, reliable, self-serve access to data across teams. • Build robust data quality checks, pipeline monitoring, and observability tooling — make data reliability visible and measurable. • Work closely with engineering teams to understand data sources, schemas, and event flows across all systems, and ensure high-quality data is captured and correctly structured at the source. • Evaluate and adopt streaming or near-real-time data capabilities where they add meaningful value to the business. • Evaluate tools and technologies in the data stack and recommend improvements with a clear rationale and view on long-term fit.

• Work with senior business stakeholders to break down business problems and design analytics solutions. • Provide technical leadership to drive best practices for data modeling, ETL, and data quality. • Design and build data pipelines to enable decision-making through analytics. • Develop automated solutions to replace manual data processes. • Refine and enhance reporting data models. • Act as a champion for data quality by proactively identifying data quality issues and coordinating with system owners to resolve issues quickly. • Build relationships with senior business leaders to gather requirements and evangelize data analytics best practices.

• Define and evolve data architecture standards and best practices. • Design and implement Data Lake and Data Warehouse/Lakehouse architectures. • Architect data pipelines (ETL/ELT) for ingestion, transformation, and consumption. • Design data models aligned with analytics and business requirements. • Ensure data governance, quality, and security across platforms. • Define integration patterns between microservices and data systems. • Support delivery teams with architectural guidance and solution design. • Collaborate with stakeholders to translate business needs into scalable data solutions. • Contribute to cloud data architecture decisions across AWS, Azure, or GCP.