KDD panel: a data scientist’s guide to startups

A data scientist’s guide to startups, a panel including:

(It may not be obvious from the list of current institutional affiliations, but many of these panelists have worked for start-ups in the past or have advised them.)

There’s video of this panel as well, but I’ve never been a big fan of watching video of talking heads when there’s a transcript available.

A couple interesting points:

      • Oren Etzioni and Usama Fayyad point out that going into data science at a startup (presumably, for people like those in the audience at KDD 2013, is not risky. I’d tend to agree, and this was basically what I figured when I left academic life – although I’d want to see it backed up with examples of people who have went back from industry to academia. There’s always the question of whether that is a one-way or two-way door.
      • there’s an interesting discussion of risk vs. reward – it’s possible that moving to a startup is a good move in expected value, but comes with more risk. Of course that’s a personal decision.

Note that this is all about venture-scale startups; are there what folks involved in the VC ecosystem sometimes call “lifestyle businesses” (something that’s not aiming for meteoric growth but supports its founders and some employees and is generally what people outside of that ecosystem would just call a “business”) that are dependent on data science?

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