Kaggle encourages a domain agnostic approach to modeling, in the sense that participants use sophisticated machine learning and statistical methods but typically have no domain expertise in the underlying data. This kind of approach to finance has historically performed poorly. [1]
Good quantitative trading is usually backed by a strong fundamental thesis and an interpretable model, which is obtained by cross-pollinating sophisticated math and statistics with domain expertise in some part of finance. That domain expertise might be in different kinds of assets, liquidity or market microstructure, but it's there.
$100k is cheap for Jane Street. If nothing else they have a new recruiting pipeline of people with demonstrable machine learning skills.
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1. I would also say this is a poor way to approach statistical analysis in most domains, and usually leads to spurious or overfit results. But the idea that you can just run a model and find patterns in pricing data is especially attractive and insidious.