Data Scientist

Deine Rolle im Team

Unsere Erwartungen an dich

Ausbildung

• 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.

Qualifikationen

• Strong SQL skills for database management and complex query optimization.
• A proven track record of proactively sourcing and engineering data (finding, negotiating access to, cleaning and, where necessary, generating data).
• Ability to create automated reporting systems and dashboards using tools like Tableau, Power BI or similar platforms.
• 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.
• Understanding of ensemble methods and explainable AI techniques for transparent decision-making.
• 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).

Erfahrung

• 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.
• Hands-on experience implementing supervised and unsupervised learning algorithms including regression models, classification techniques, clustering methods, and time series analysis.
• Experience designing and implementing ETL pipelines for processing datasets from multiple sources, with skills in data cleaning, transformation and quality assurance at scale.
• Experience working with large, messy, real-world datasets.
• 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.
• 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.
• Experience with multimodal data fusion.

Unser Angebot