Deep Learning Specialist, Treefera

• Strong background in Machine Learning, Deep Learning, and Applied Statistics
• Experience with time-series modelling. Familiarity with building and backtesting
• Proficiency with the Python scientific stack: scikit-learn, PyTorch, scipy etc
• Familiarity with version-controlled, reproducible workflows (AWS/cloud infrastructure, Git, Weights&Biases/experiment tracking)

Desirable

• You’ll join a global, cross-functional team that values rigour, curiosity and working close to real-world challenges. Whether your focus is AI, climate, product or operations, you’ll have space to contribute meaningfully and make an impact from day one
• If you’re excited by complex problems and want to help reshape how nature is valued in real-world decision-making, we’d love to hear from you
• Design, implement, and evaluate ML/DL models for processing alternative data sources (satellites and weather data) for risk and trading signals , including:
• Forecasting environmental or risk-related signals (e.g. increasing weather and climate volatility, agricultural stress indicators) )
• Use remote sensing datasets (e.g. Sentinel-1, Sentinel-2, GEDI, other optical and radar missions) and climate data to build vegetation stress signals, landcover classifications and land-surface conditions
• Develop time-series and forecasting models to detect and anticipate environmental changes and their impacts on global markets
• Collaborate closely with the wider AI, Science, Product, and Engineering teams to:
• Translate business questions into robust modelling problems
• Turn research prototypes into scalable, reliable AI pipelines that deliver actionable information
• Help shape modelling standards, documentation, and reproducibility within the AI team (e.g. experiment design, evaluation protocols, uncertainty treatment)
• Communicate methods, assumptions, and results clearly to technical and non-technical stakeholders, including limitations and uncertainty