Staff Data Scientist (Forecasting)
Confirmed on the employer's own hiring board on Oct 10, 2026. First seen by Alion on Oct 8, 2026.
Join our team as a Staff Data Scientist specializing in forecasting. In this senior individual contributor role, you will be responsible for improving the accuracy of our harvest forecasts for tomato, pepper, and cucumber. You will work closely with data scientists, crop scientists, and growers to identify and solve problems in the entire forecasting system. This role is based in Amsterdam and requires a strong background in time series and probabilistic forecasting, as well as experience in production-grade Python and clear communication with stakeholders.
Missions
- Assumer la responsabilité de l'exactitude des prévisions de récolte pour les tomates, les poivrons et les concombres, en identifiant et en résolvant les problèmes liés aux données d'entrée, à l'échantillonnage des plantes ou aux plans des cultivateurs.
- Concevoir et construire des modèles hybrides qui combinent la physiologie des cultures et le climat des serres avec des méthodes statistiques et d'apprentissage automatique, et mettre en place un système d'évaluation robuste.
- Collaborer avec les ingénieurs pour déployer vos modèles en production et rester responsable de leur comportement, tout en élevant le niveau de l'équipe de science des données par le biais de revues, de défis et de mentorat.
Profil recherché
- You have shipped models that broke when the world changed (a new season, a new site, a new customer) and fixed them, more than once. You can tell us what broke each time- Production-grade Python (pandas or polars, scikit-learn, PyTorch or JAX, PyMC or similar) and solid engineering habits (Git, testing, CI/CD, Databricks, AWS), so your models run in production without you
- Clear communication with growers, product and engineering alike
- An MSc or PhD background and the habit of reasoning from first principles
- Validation that holds up: backtests against strong baselines, error analysis tied to business impact, drift monitoring, and evaluation tools other people kept using after you built them
- You work AI-native: coding agents and LLM tools are how you work every day, and you know where they fail
- Around 10 years of hands-on modelling or forecasting, with models of physical or real-world systems that ran in production for more than one season. Energy, weather, process industry, plant breeding, pharma, batteries or trading: the field matters less than the fact that it was real
- Deep expertise in time series and probabilistic forecasting: Bayesian and hierarchical models, gradient boosting, deep learning for sequences, and uncertainty quantification on small, irregular datasets
- You have worked next to people with a higher standard than yours, and can say what they taught you
- You have taken a forecast people did not trust and made it one a business commits money on
- Physics-informed ML, mechanistic or process-based models, data assimilation (for example Kalman filtering) or system identification
- A degree in engineering, physics, applied mathematics, statistics or econometrics
- Publications, talks or open-source work on forecasting or scientific ML