Data Scientist

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NRC

WebsiteLinkedInAll Job Openings

11 - 50 employees

NRC, créée en 1997, est un intégrateur informatique, spécialisé dans l’intégration de solutions de gestion pour les PME et est devenue un acteur de référence dans le déploiement de solutions liées aux infrastructures pour les plus grosses structures.

📋 Description

• Support the design and implementation of CLEAR’s data architecture. • Partner with developers and data engineers to design and stand up the environment needed to train and fine-tune models (including data ingestion pipelines, compute and GPU resources, experiment tracking and MLOps tooling) actively shaping that environment rather than waiting for it to be provided. • Develop machine learning and deep learning models that integrate multiple data streams to detect early indicators of humanitarian crises, combining earth observation and satellite imagery, conflict event data, climate monitoring, economic indicators, and population movement patterns into unified risk assessment frameworks. • Build computer-vision and remote-sensing models on satellite and aerial imagery (for example flood-extent mapping, building and settlement detection, displacement-site monitoring, infrastructure damage assessment and land-cover change detection) and fuse these earth observation outputs with non-imagery signals. • Contribute to building automated alert systems that identify emerging crises, calibrating prediction algorithms for different crisis types. • Create ensemble modelling approaches that combine traditional statistical methods with advanced AI techniques. • Fine-tune and adapt foundation models and large language models to humanitarian use cases such as document triage, multilingual report analysis and situation summarisation. • Explore venues for adapting models to evolving crisis conditions through reinforcement learning systems. • Implement impact-based forecasting systems that translate meteorological, conflict, and economic predictions into specific humanitarian consequences such as displacement volumes, food insecurity levels, and infrastructure damage estimates. • Build decision trees and recommendation engines that guide field staff through systematic needs assessment processes informed by predictive analytics and historical response data. • Create automated reporting systems and interactive dashboards that enable field teams and leadership to access real-time data for rapid response activities.

🎯 Requirements

• Advanced degree in Data Science, Statistics, Computer Science, Physics, Engineering, Economics or a related quantitative field, with a minimum of 5 years of professional experience in applied data science. • Demonstrated experience designing, training and fine-tuning deep learning models (e.g. CNNs, recurrent/sequence models and transformers), including how to structure training runs, manage compute, and diagnose and improve model performance. • Advanced proficiency in Python for statistical analysis, machine learning and data manipulation, with experience in key libraries including pandas, NumPy, scikit-learn, TensorFlow, PyTorch and Keras. • Strong SQL skills for database management and complex query optimization. • Hands-on experience implementing supervised and unsupervised learning algorithms including regression models, classification techniques, clustering methods, and time series analysis. • A proven track record of proactively sourcing and engineering data (finding, negotiating access to, cleaning and, where necessary, generating data). • Experience designing and implementing ETL pipelines for processing datasets from multiple sources, with skills in data cleaning, transformation and quality assurance at scale. • Ability to create automated reporting systems and dashboards using tools like Tableau, Power BI or similar platforms. • Experience working with large, messy, real-world datasets. • Understanding of model deployment and MLOps practices, and comfort working with engineers to provision the infrastructure models need. • Fundamental skills with version control software and collaborative development. • Fluency in written and spoken English. Other languages are an asset. • Practical experience working in low-data or data-scarce settings, including transfer learning, few-shot learning, data augmentation and synthetic data generation to overcome limited training data. • Experience implementing and fine-tuning large language models (LLMs) for applied tasks. • Experience with natural language processing techniques for analyzing reports, social media or news data relevant to crisis monitoring. • Experience using GenAI for automated analysis of large volumes of documents — extracting key themes, sentiment analysis and identifying emerging trends across multiple contexts and languages. • Understanding of ensemble methods and explainable AI techniques for transparent decision-making. • Experience working with earth observation and satellite imagery; optical (e.g. Sentinel-2, Landsat) and radar / SAR (e.g. Sentinel-1), alongside commercial high-resolution sources, and with geospatial tooling such as Google Earth Engine, rasterio / GDAL, xarray and geopandas. • Applying computer vision and deep learning to imagery (semantic segmentation, object detection, change detection) for humanitarian tasks such as flood-extent mapping, damage assessment, settlement and displacement-site detection, infrastructure monitoring and population estimation (a strong asset). • Experience with multimodal data fusion.

🏖️ Benefits

• Duty station: Remote (Germany, France, UK or Belgium) • Contract: Fixed term (2 years) • Travel: Up to 10% • NRC is an equal opportunities employer. We are committed to diversity without distinction to age, gender, religion, ethnicity, nationality, and physical ability. • We think outside the box, encourage ideas, and give responsibility to all employees at all levels. You will have many opportunities to be heard and take the initiative.