The other day I was talking with an acquaintance of mine who is the CEO of a local startup in London and I was astonished to discover that the faux data science trend that plagues the West Coast of the US is in London too. The British capital is not only the home of the top A.I. startup, Deep Mind, which was acquired by Google lately, but it’s also the place where a great deal of data scientists have come about. Also, it prides itself for its pragmatism and for how grounded it is, especially when it comes to science. Still, somehow many of the data science practitioners in this great city are what I call faux data scientists, professionals who use the term “data scientist” on their business card, even though they have no real relation to the field.
Contrary to a real data scientist, the profile of whom I describe in my first book, a faux one is both confusing and confused. A (true) data scientist focuses on predictive analytics, usually through the use of ML systems and lately systems powered by A.I., even though he also makes use of Statistics in various ways. A faux data scientist, on the other hand, relies mainly on Stats and some of the most rudimentary ML models (though he may use ANNs too, without bothering to configure them properly or even read up on the corresponding scientific literature). While a data scientist relies on science to obtain insights and makes use of various methods for communicating her results, a faux one creates pretty plots that may or may not convey any real findings, though they may impress his audience. A faux data scientist usually outperforms the real data scientist in another thing: BS talk. The real data scientist tends to be more humble and veers away from extravagant claims about what the data can yield. This is particularly true if he comes from an academic background. However, the faux data scientist has no inhibitions when it comes to making excessive promises and delivering insights that would qualify for a Nobel prize, if they held water. In other words, the faux data scientist is full of hot air, but manages to hide all the BS of his methodology behind fancy talk brimming with buzzwords and anything else he could come up with in order to convince (or please, rather) his audience.
Unfortunately the damage that a faux data scientist goes beyond his personal work. Given enough time, the managers of the corresponding projects will see through all the BS this “data scientist” does. That’s the time when the faux data scientist will probably leave or go about to start his own company. However, the loss of confidence in the profession is bound to linger. And even though it took years of hard work and equally hard research in the field to build this confidence, it’s not as strong as it needs to be in order to sustain this kind of damage. Of course the faux data scientist doesn’t care because he’s in it for the money, the reputation, or whatever other personal gain his ambitions dictate. He hasn’t done any research on the science behind the techniques and is adept only at applying other people’s work, through the myriad of Python and R packages that are out there. But it’s not all bad. As he is bound to talk his way into all sorts of situations, once the field no longer serves his purposes, he is bound to jump ship to some other field (whatever is trendy at that time) and never look back.
However, the faux data scientist is not to blame entirely for all this. She is just taking advantage of the situation, particularly the fact that the hiring managers look for 1) x years for experience in the field, experience they are unable or even incapable of assessing accurately, and 2) someone with “excellent communication skills”, especially when it comes to showcasing projects brimming with eye candy and buzz words the management will recognize. So, unless we start seeing through the BS of the faux data scientists and treat them the way they deserve, this situation is not going to go away any time soon…
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.