S
Source.ag
Staff Data Scientist (Forecasting)
Skills
Deep LearningPythonData SciencepandasPyTorchDatabricksMachine LearningAIAWS
16 days ago
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Important information
Contract type:
Permanent
Salary:
€107,000 – €120,000 / year
Location:
Amsterdam, Netherlands
Work mode:
Onsite
Published on:
8 October 2026
What they need
- We are looking for a Staff Data Scientist who has done this before: taken a forecast of a physical system that people did not trust, and made it a number a business commits money on
- The difficulty is not one clever model
- It is the whole system: data quality, how plants are sampled, growers changing their harvest plans, and then the model
- There may be ten things to fix before the forecast is good, and only some of them are in the model
- You look at the whole system end to end, find the places that matter, do the hardest work yourself, and carry it all the way to something growers use
- This is a senior individual contributor role, technical all the way, with no people management
- You report to our Director of Engineering and join our staff engineers, the senior group that works across teams with the CTO and sets the technical bar
- Day to day you work with our data scientists, crop scientists and directly with growers
- Harvest forecasting is the first problem
- The same kind of problem shows up across our other crop models, and we expect your work to reach there too
- The role is based in Amsterdam, because you cannot see the whole system from a distance
- You need to be in the room with the data scientists, the crop scientists and the people who talk to growers every week
- Own the accuracy of our harvest forecasts for tomato, pepper and cucumber, especially in the unusual weeks, from diagnosis to a fix growers use
- Decide which problem is worth solving first, including when the fix is in the input data, the plant sampling or the grower’s plan rather than the model
- 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, so the next ten fixes get cheaper
- 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- 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- Intro call with the hiring manager or our People & Culture team
- One story: a time a hard project changed direction because of what you did. We go deep until we understand what you did yourself
- Workshop on a real problem. We send you a write-up of how a greenhouse works, how plants are measured and where our forecast struggles, then work through it together, with your own tools including AI. Agreeing with our diagnosis earns nothing. A good next move does
- Final conversation and references
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