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Darwin Recruitment are hiring for a Data Science Manager for a business in Luxembourg that are a climate-tech / geospatial-data company that builds and operates satellites to gather thermal infrared and multispectral imagery of Earth. Their mission is to capture and deliver high spatio-temporal resolution thermal infrared data, currently not available from other sources, to provide insights that benefit the environment, enhance food and water security, and serve other critical applications. Their downstream applications team in Luxembourg combines best-in-class remote sensing, machine learning, and cutting-edge fusion approaches to thermal imagery to deliver innovative and scalable solutions to the agri-food industry.
Job Responsibility:
Lead and mentor a team of data scientists developing and delivering high-quality scientific models and solutions
Work closely with cross-functional teams, including product and program management, and translate business goals into reliable, scalable systems
Provide hands-on technical guidance, development, code reviews, and supporting the growth of your team
Requirements:
10+ years of data science experience, with a solid understanding of data science principles and practices
2+ years in a leadership role, with proven experience in building and coaching high-performing data science teams, including defining team objectives and aligning them with organizational goals
Experience in Earth observation, geospatial, imaging science, or computer vision field
Knowledge of supervised and unsupervised learning techniques and principles, including regression, classification, clustering, and recommendation systems
Proficiency in statistical analysis, hypothesis testing, and experimental design (e.g., A/B testing)
Experience working with cloud services and infrastructure
Demonstrable experience building effective working relationships with engineering teams
Strong team player with demonstrated ability to take ownership and drive execution
Proficiency in English (written and spoken)
Nice to have:
Master’s or PhD degree in environmental sciences, mathematics, physics, or relevant engineering field
Experience with geospatial and earth observation tools, including satellite data processing, Rasterio/GDAL, and GIS tools
Knowledge of surface energy balance modeling, soil physics, hydrology, or related domain
Experience with advanced modeling techniques like deep learning (e.g., neural networks, CNNs, RNNs) and ensemble methods
Understanding of MLOps best practices for monitoring, versioning, and maintaining models in production
Experience with High Performance Computing (HPC) platforms
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