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I recently came across this interview (thanks Dharini for the link!) with Nick Chamandy, a statistician a.k.a a data scientist at Google. I would encourage you to read it; it does have some great points. I found the following snippets interesting: Recruiting data scientists: When posting job opportunities, we are cognizant that people from different academic fields tend to use different language, and we don’t want to miss out on a great candidate because he or she comes from a non-statistics background and doesn’t search for the right keyword. On my team alone, we have had successful “statisticians” with degrees in statistics, electrical engineering, econometrics, mathematics, computer science, and even physics. All are passionate about data and about tackling challenging inference problems. I share the same view. The best scientists I have met are not statisticians by academic training. They are domain experts and design thinkers and they all share one common trait: they love data!...

My ongoing conversations with several people continue to reaffirm my belief that Data Science is still perceived to be a sacred discipline and data scientists are perceived to be highly skilled statisticians who walk around wearing white lab coats. The best data scientists are not the ones who know the most about data but they are the ones who are flexible enough to take on any domain with their curiosity to unearth insights. Apparently this is not well-understood. There are two parts to data science: domain and algorithms or in other words knowledge about the problem and knowledge about how to solve it. One of the main aspects of Big Data that I get excited about is an opportunity to commoditize this data science—the how—by making it mainstream. The rise of interest in Big Data platform—disruptive technology and desire to do something interesting about data—opens up opportunities to write some of these known algorithms that are easy to execute without any performance penalty. Run K Me...