Staff Model Engineer (Forecasting)

HORTIHEROES | AVAG | WORLD HORTI CENTER Jobs for Growth is powered by: HortiHeroes, World Horti Center & AVAG Staff Model Engineer (Forecasting) Every week, a tomato grower tells their buyers how many kilos they'll 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, and our Harvest Forecast for tomato, pepper, and cucumber is one of the inputs to that weekly number. Getting an average week right isn't 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 end of the season. Those are the weeks when growers need a forecast most, and when forecasts, ours included, are trusted least. - 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 can't 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. 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 is made. Telling the two apart is part of the job. - Strong science, messy greenhouses. Decades of crop and climate science exist. Data alone won't rediscover it, and the science alone doesn't fit a real greenhouse. - People act on the number. A forecast that's confidently wrong costs a grower real money, so the uncertainty has to be honest. The difficulty isn't one clever model. It's 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 ...

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