Staff Data Scientist, Forecasting

The problem Every week, a tomato grower tells their buyers how many kilos they will deliver three weeks from now. Contracts, trucks and prices are set on that number. Source builds AI software that greenhouse growers in Europe and North America use for decisions like this one, and our Harvest Forecast for tomato, pepper and cucumber is one of the inputs to that weekly number. Getting an average week right is not the hard part. The value is in the weeks when the crop does something unusual: a heat wave, a sharp drop after a peak, the start and the end of the season. Those are the weeks when growers need a forecast most, and when forecasts, ours included, are trusted least. Why this is hard: - Slow feedback. You can replay past seasons with any model. But a new model in production shows its first real result only three to four weeks later, and the weeks that matter come only a few times a year. You cannot A/B test a crop. - Thin, noisy data. A few plants, measured by hand once or twice a week, stand in for a whole greenhouse. Sensors drift. Every site, variety and grower strategy is a little different. - Not all of it is biology. Part of every swing comes from the plant, and part from decisions the grower makes after the forecast was made. Telling the two apart is part of the job. - Strong science, messy greenhouses. Decades of crop and climate science exist. Data alone will not rediscover it, and the science alone does not fit a real greenhouse. - People act on the number. A forecast that is confidently wrong costs a grower real money, so the uncertainty has to be honest. 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. What you'll do - 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