[fhuszar]

The problem with this is that people's skills combine non-linearly. Just like a cluster of 100 machines is not 100 times faster than a single machine. After all, why do we need kite surfers, just tie a surfer and a kite jumper together.

OK, so let's say I'm the scientist working alongside a couple of computer science people. Now, every time I have to remove a comma from the files they exported for me, should I ask them to please write a magic line into the shell for me. Every time I have to parse data in json or whatever else I don't know anything about I wait for them to do it for me. Every time data has to be loaded from a database, I explain what data I want, wait for the computer science person to write and execute a query for me? Just doesn't work this way.

When working with data you have to be able to experiment, and to experiment you have to have an idea about what's possible and what's not. If, as a scientist you do not understand what these practical tools can do for you, your experimentation will be severely limited. You have to be able to pair up the most promising mathematical approach with the simplest techniques that get you there. This is very hard to do, unless the same person knows about both maths and computer tools.

But more fundamentally, machine learning (or data science) does not equal statistics + computer science. The skillset you need to be a powerful part of the team is not simply a union of various computer science and statistics skills. It requires a different mindset.