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Comment by kamaal | Hacker News Reader

>>There are plenty of data scientists working on OS hacks, compilers, and other "hard engineering" topics.

Can you give some examples on this? Seems very interesting.

I encourage you to peruse the Github page of the R celebrity Hadley Wickham. While trained as a statistician and employed as a professor of statistics at Rice, he writes quite a few R packages that deal with more traditional development roles (unit testing and debugging, not to mention Plyr and GGplot): https://github.com/hadley?tab=repositories

Also, Duncan Temple Lang, another professor of statistics, has done some really amazing work with R compilers and CUDA interfaces. https://github.com/duncantl?tab=repositories http://www.omegahat.org/

Rob Zinkov (https://github.com/zaxtax) is a first-rate hacker. He has a popular ML blog (http://zinkov.com/) and organizes the Los Angeles machine learning users group.

Nothing comes off the top of my head, and it's not like they get to specialize in compilers. They mostly end up doing one-off hacks to make an existing algorithm more performant.

What's fun about machine learning is that it touches so many other parts of computer science. You could be at a high level writing DSLs in Clojure to make it possible for statisticians to specify their models directly, or you could go to the low level and write GPU code.

The general rule is that if your boss thinks he can do your job, you lose. If he doesn't think that, you win. When you're a data scientist, your odds are much higher of coming out in the second category.

To your last point, it depends on what role you currently serve to your company. If you're among statisticians, you make yourself valuable by knowing more about computer science and programming than the rest of the group. If you're amongst engineers (probably more common than the former--at least on HN), knowing probability and statistics gives you that edge.

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