Not the person you're replying to, but there are tons of models that aren't neural networks. Triplebyte used to use random forests [1] to make a decision to pass or fail a candidate given a set of interview scores. There are a bunch of others, though, like naive Bayes [2] or k-nearest-neighbors [3]. These approaches tend to need a lot less of a training set and a lot less compute than neural networks, at the cost of being substantially less complex in their reasoning (but you don't always need complexity).
[1] https://en.wikipedia.org/wiki/Random_forest
[2] https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Trainin...
[3] https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm