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If the number of dimensions is high, building a model by hand could be extremely difficult, and using ML makes sense.

If you have 100 examples and a high number of dimensions you will end up with a very over-fitted model.

Not necessarily. Cross-validation can give you a valid estimate of out-of-sample performance even if you have more dimensions than samples, and even if some of the features you have are (sporadically) perfectly correlated with the target variable. See https://stats.stackexchange.com/questions/295626/does-cross-...

The terms you're using - they come from Machine Learning, which comes from the same division of mathematics as ordinary statistics. And that's how ML is educated at the universities anyway.

So if you've got an analyst sitting on that issue - then your problem is solved anyway.

So again, why hire somebody with a trendy specialty who is <probably> full of BS, when you can hire someone with some respect for classics?

I never said anything about who to hire.

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