If you look up data science or machine learning jobs you’ll find an ocean of postings that ask for a PhD in machine learning or related field with 3+ years’ experience. Why a PhD? Why not a Masters’ degree? And why not 2 years’ experience? Or 18 months? In reality, there aren’t a lot of cases where the work of an ML engineer actually requires them to have a PhD. So what’s the real point of asking for one?
A vastly disproportionate number of hires are the result of referrals from employees who already work at a company. So your best way in, nine times out of ten, will be through a relationship with someone who works at your target company, rather than a generic channel, like a jobs board. Relationships are great because they give you a signal boost, but they also make it much more likely that you’ll get feedback on your application. But how do you build meaningful relationships with established data scientists?
In startup lingo, a “vanity metric” is a number that companies keep track of in order to convince the world — and sometimes themselves — that they’re doing better than they actually are. Vanity metrics are everywhere, and they can really hold us back when we optimize for them, rather that optimizing for something that matters. They cause us to spin our wheels, and not understand why our hard work isn’t leading to results.
Do you need a graduate degree for data science? Maybe so. Maybe not. In a rapidly developing field like data science, convention can often lag considerably behind what’s optimal. As a society, our perception of the value of graduate education is one of the aspects of conventional wisdom that’s most badly in need of catching up to reality. None of this means that formal education, or even graduate degrees aren’t worth obtaining, of course. But no one should take the need for a Master’s or a PhD for granted: you might want to rethink your strategy.