Staff Data Scientist (Forecasting)
Posted Updated
Description
- 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
- At Source, we value diversity of background and perspective. People assess their own qualifications differently, so please apply even if you do not tick every box. We are looking for the right person, not a perfect CV
- 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