from sklearn import *

and other dead-giveaways that you’re a fake data scientist

Like most news outlets writing about “hackers”, fake data scientists happily use any random code they find online (image source: Picography)

These days it seems like everyone and their dog are marketing themselves as data scientists—and you can hardly blame them, with “data scientist” being declared the Sexiest Job of the Century and carrying the salary to boot. Still, blame them we will, since many of these posers grift their way from company to company despite having little or no practical experience and even less of a theoretical foundation. In my experiences interviewing and collaborating with current and prospective data scientists, I’ve found a handful of tells that separate the posers from the genuine articles. I don’t mean to belittle self-taught and aspiring data scientists — in fact, I think this field is especially appropriate for passionate self-learners —but I definitely mean to belittle the sort of person who takes a single online course and ever after styles themselves an expert, despite having no knowledge of (or interest in) the fundamental theory of the field. I’ve compiled this list of tells so that, if you’re a hiring manager and don’t know what you’re looking for in a data scientist, you can filter out the slag, and if you’re an aspiring data scientist and any of these resonate with you, you can fix them before you turn into a poser yourself. Here are three broad domains of data science faux pas with specific examples that…