Learning & Development Specialist at JBT Marel creating training materials and solutions. Managing initiatives to enhance the Poultry Academy and support technical excellence across the business.
About the role
- Deep Learning Specialist developing AI-driven solutions for nature-based assets at climate-tech startup. Collaborating across teams to design and implement ML/DL models for risk and trading signals.
Responsibilities
- Design, implement, and evaluate ML/DL models for processing alternative data sources (satellites and weather data) for risk and trading signals.
- 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.
Requirements
- 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).
- Experience with risk modelling, financial time series and portfolio optimisation techniques.
- Experience working with weather and climate data, particularly CMIP archives and weather forecast data.
- Experience with geospatial techniques (rasterio, xarray, geopandas, GDAL) and remote sensing data (optical, radar, LiDAR) is beneficial.
- Familiarity with MLOps practices (containerisation, CI/CD, model monitoring) is a plus.
- Prior experience in a startup or fast-moving product team.
Benefits
- Access competitive pay, equity and meaningful benefits
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