data scientists / statisticians mostly need access to computing power, which is fairly cheap these days.

This is true for each marginal data scientist. But there's a catch, which is that those folks need data. Collecting and promulgating that data, in the application domains we care about, can sometimes be very costly. You might want to consider some of those as part of the cost for the data science.

For example, many countries are spending a huge amount of money on electronic health records, in part to allow better data mining. The health records aren't primarily for scientific purposes, but making them researcher-friendly is a big indirect cost. Similarly, the census is a very expensive data-collection process that enables a lot of "cheap" analytics downstream.

While each data scientist might be cheap, there was a big up-front investment, at the national level, to enable them.

Well, statisticians can be thought of as kinda-mathematicians and George Pólya has remarked that

Mathematics is the cheapest science. Unlike physics or chemistry, it does not require any expensive equipment. All one needs for mathematics is a pencil and paper

Otherwise, e.g. pharma research and biochem in general can get pretty expensive.

Actually, my uninformed guess (from casual familiarity and friendship with people from various fields) is that physics and chemistry are cheaper than biology to conduct. There's expensive equipment in Phys/Chem but it can be re-used over and over again by multiple labs. Biology on the other hand has major recurring costs in the form of maintaining animal populations and greater degree to which replication is important. And then things get cheaper again in the psych/social sciences, where experiments are often either computerized or conducted by undergraduates for credit and conducted on volunteers.

Basically, if you graph xkcd!purity by price, I think it is bell shaped with Biology at the peak. In an absolute "per researcher, per experiment" sense. That's not to say that biology might not be "cheaper" in terms of return on investment from an EA standpoint.