$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.

10 thoughts on “$20K Waiter to $100K Data Scientist in 3 Months?

  1. So, where to begin? First, this wasn’t a high school dropout who took a course and jumped from hopeless to a career, this was a guy with an A- average and a university BS Math degree who went un/under-employed for two years after graduation. This is NOT a “worker turns to coding”, it’s a college grad finally gets a job after two years. The $100k salary is a tad high, but this is Silicon Valley and data science is apparently as hot there as down here in Los Angeles. Actually, in Los Angeles these days someone with actual data science *experience* should get $150k as a worker and $200k as a lead. It’s hot. Or was last year, it may have already cooled about 20-30% by now.

    There was a time, umpty years ago, where I too was hired for my “expertise” of about two weeks with a particular tool. It happens. Into the lower reaches of the field, especially. I guess Minton should do fine, I did. And the whole deal about “data science” is another discussion, don’t even get me going on that.

    So what does this teach us about “coding academies”? Nothing. Well, that employers are just crazy about buzzwords. Back in my day I didn’t take a formal course, I just bought a compiler – I knew the compiler even *existed*. That’s what I was hired for! A better employment process would have found Minton six months earlier and SENT him through a training class on the company’s money, probably paid him less until and unless he finished the course and proved his worth on the job. Much more rational. Is how companies worked back in the day.


    • It does indeed tell us a lot about how employers act regarding “hot” technologies and buzzwords. However, the data science field really is quite different from software development. I agree with the thrust of what you are saying, but some of your analogies are not quite apt. I would submit, for instance, that giving a job applicant a couple of days to do analysis of a data set is quite different from having an applicant for a software position write a short code segment. I’ll say this: I often find that students who are excellent programmers have real trouble with data analysis.


    • Yeah. I pretty much agree. This is just the latest fad for brain-dead corporations who can’t figure out how to hire (or retain) good people.

      As for the news media, they first wrote a headline and went shopping for some “facts” to match up with their pre-existing narrative. In other words, the usual.

      The real story here is “Math Major was under-employed for two years after graduation”, but that is not heart-warming enough to get media attention.


      • Not completely. Very few new Bachelor’s graduates, even in computer science let alone math, make $100K right out of school. So there IS a story there, and of course the whole idea of a boot camp is newsworthy too.


  2. > So, yes, it does sound plausible that Minton was hired into a $100K data scientist job in spite of limited background.

    I found it interesting how the story is talking about someone who works with “Big Data” and seems to draw so many conclusions from a single data point! As you say, Milton already had some positive qualifications, being a math major with enthusiasm for the field. Also, the field of “Big Data” may possibly be temporarily short on qualified workers. I’ve been working on replicating studies of some economists who support raising the cap for H-1B and other visas and I’m a bit surprised that nobody else seems to have tried to replicate those studies. The latest one that I’ve worked on replicating is a recent study by Peri, Shih, and Sparber published in the Journal of Labor Economics. My current analysis showing one obvious typo and a deeper problem of overfitting of data is at http://econdataus.com/jole_pss.htm .

    Of course there are tons of Big Data out there so the fact that this area seems overlooked may not be that surprising. Also, there is no money on this side of the issue. You might say that all of the “Big Money” is on the other side. Hence, it’s not just the skill that one has in analyzing the data. One also has to have other characteristics (such as the right age or area of study) to be hired.


    • If you think the situation you describe is not already occurring right now, then you are sadly mistaken. I would say at least half of data science positions are being filled by H-1Bs and the like, at least in the Bay Area.


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