$20K Waiter to $100K Data Scientist in 3 Months?

A number of readers have called my attention to the odd story of Paul Minton. After college graduation in 2011, he worked as a waiter with income about $20,000 per year in San Francisco for a couple of years. But after a 3-month “boot camp” course in data science, he somehow landed a $100K job in the field.

So, is this a misleading PR stunt by industry lobbyists, as some of my readers suspect? Could be, but here I’ll tell you why it’s plausible, and then address the question of whether graduates of boot camps are generally skilled enough to be paid that much right out of boot camp. (Short answer: No.)

First, note that we’re not talking about software developers here. Data scientists (a term I dislike but find convenient to use anyway) do write some code, but only as a means to an end, which is to develop predictive models. It’s the Big Data field you’ve been hearing about (though often the data sets are not that large).

In one sense, the barriers to entry are low. Someone with good quantitative intuition and drive can do well, the most celebrated case being that of Nate Silver, well known for his accurate predictions of the last two presidential elections, in spite of not having a broad knowledge of the field. (He didn’t do well when he tried predicting the UK election, showing that his intuitive understanding of US politics was his key to success, rather than knowledge of statistical methods.) Actually I’ve known guys wearing backwards baseball caps — nothing wrong with caps, just making the point that they were not PhDs — who’ve developed successful data science consulting businesses with almost no background.

According to Minton’s LinkedIn entry, he actually had some prior background in data science, including three MOOCs (large online courses). While these courses tend to be shallow, one can at least learn the terms and get some experience with real data. In fact, one person won a Kaggle data competition armed with nothing more than a MOOC from Coursera, where Minton had taken courses prior to the boot camp.

The boot camp included several useful projects, again according to Minton’s LinkedIn page. The company that hired him probably gave him a data set to analyze as part of the interview process, and he would have felt comfortable with it.

Most importantly, it’s clear that Minton talks a good game. I don’t mean that pejoratively; on the contrary, he appears to have real enthusiasm for this field, which is a huge point. He follows Kaggle, for example, in the same way that some follow the NBA. This trait is sadly missing in most students I interact with (grad and undergrad, domestic and foreign, UCD and elsewhere). Employers love this, for good reason.

So, yes, it does sound plausible that Minton was hired into a $100K data scientist job in spite of limited background. But that doesn’t mean employers who hire such people are making good decisions. There really is a major value to employers in hiring people who have extensive background in the subject, rather than the Cliff Notes version. And though I’m speaking generally here, some of Minton’s postings do suggest to me that he would benefit from a deeper pursuit into the field.

What is most worrisome about all this is that I know two older people, who live in the Bay Area like Minton, who are much more qualified, yet cannot get a job. Both have better formal training, both have more work experience, both have good “soft skills,” yet employers pass them by. Good for Minton, whom I’m sure will be a big success, but something is very wrong here. The real question is not the one my readers brought up — is Minton’s story for real? — but rather, why these two older workers are being ignored.