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Comment by jboggan | Hacker News Reader

I've found that a lot of companies are looking for data scientists but many of them have very different ideas of what that means. This makes for some interesting interviews.

I recently moved to SF and am currently interviewing for data science positions - particularly ones involving social networks and applied graph theory - so drop me a line if you know anyone who is dealing with that problem space.

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Just checked out your LI profile (fellow data science guy here) -- I think you basically need a bit more work experience or some github code to show yourself off. The big data guys like Google who have best practices, brand, and provide great onboarding should be your focus IMHO.

Quick question: What do you classify as work ex? I do mostly iOS programming, but I've been playing with Hadoop + the commoncrawl.org crawl data. Basically, I guess, what level of stats do you need to be comfortable with to call yourself a data scientist?

Following Gladwell's 10000 hour rule, I would say you could probably call yourself a data science after 1000+ hours experience working with datasets successfully. As far as the math goes you should be able to do regression analysis, you don't need to know tons of stats but you do need to know stats and probability essentials (first few classes at a good school) deeply. I like this Wikipedia entry on "mathematical maturity": http://en.wikipedia.org/wiki/Mathematical_maturity; apart from writing proofs, it is very relevant.

At the end of the day, analytics is measured by effectiveness and appropriateness, not complexity. Simple regressions will do fine, but the "art" is to choose the right questions to ask. Typically if you're in a business setting that boils down to efficiency problems and maximizing time/money/happiness/etc. Dealing with these real-world problems = work exp.

Thanks, I'm trying to find relevant ways to get that experience and working on some more sample code as well. What would you consider an "entry level" data science job?

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