Staff Model Engineer — Hybrid Crop Forecasting & ML

Source; Amsterdam; Ibrido; EUR 120.000 - 180.000; Tempo pieno

Vantaggi offerti da questo lavoro

Hybrid work

Meals provided

Pension 4.5%

Mental health support

MacBook Pro

Travel allowance

Learning budget

Home office budget

WiFi stipend

Flexible holidays

Company events

Descrizione del lavoro

Source in Amsterdam is seeking a Staff Data Scientist to own harvest forecasts for tomato, pepper, and cucumber, guiding improvements from diagnosis to implemented fixes used by growers. You will work with data scientists, crop scientists, and growers to design hybrid models that blend crop physiology with climate data, and you’ll be responsible for production deployment and monitoring.

The role emphasizes end-to-end ownership, production-ready modeling, and mentoring colleagues to raise the

Competenze

• Around 10 years of hands-on modeling or forecasting, with models that ran in production for at least one season.
• Shipped forecasts that were trusted and used in business decisions despite changes in data or environment.
• 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.
• Production‑grade Python (pandas or polars, scikit‑learn, PyTorch or JAX, PyMC or similar) and solid engineering habits (Git, testing, CI/CD, Databricks, AWS).
• An MSc or PhD background and the habit of reasoning from first principles.
• You work AI‑native: coding agents and LLM tools are part of how you work every day.

Mansioni

• Own the accuracy of harvest forecasts for tomato, pepper and cucumber, especially in unusual weeks, from diagnosis to a fix used by growers.
• Decide which problem is worth solving first, including whether the fix is in input data, plant sampling, or the grower’s plan.
• Design and build hybrid models that combine crop physiology and greenhouse climate with statistical and machine learning methods.
• Build the evaluation backbone: backtests on past seasons, strong baselines, uncertainty calibration, and production monitoring, to reduce future fixes cost.
• Ship your models to production with our engineers, and stay responsible for how they behave there.
• Raise the bar of the data science team through review, challenge, and mentoring.

Conoscenze

Time series forecasting

Probabilistic forecasting

Bayesian methods

Hierarchical models

Gradient boosting

Deep learning for sequences

Uncertainty quantification

AI-native / LLM tools

Production-grade Python

Engineering Habits

Communication with stakeholders

Formazione

MSc or PhD in a quantitative field

Strumenti

Pandas/Polars

Scikit-learn

PyTorch

JAX

PyMC

Git

Testing

CI/CD

Databricks

AWS

Descrizione del lavoro

Source in Amsterdam is seeking a Staff Data Scientist to own harvest forecasts for tomato, pepper, and cucumber, guiding improvements from diagnosis to implemented fixes used by growers. You will work with data scientists, crop scientists, and growers to design hybrid models that blend crop physiology with climate data, and you’ll be responsible for production deployment and monitoring.

The role emphasizes end-to-end ownership, production-ready modeling, and mentoring colleagues to raise the