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.
A lot of data science career advice follows a similar law: there are posts aimed at complete beginners, posts aimed at veteran software engineers, and posts designed to help junior data scientists hone their skills. All of this noise makes it difficult for many aspiring data scientists to know where to invest their time as they look to transition into the field. At the end of the day, whether you’re a software engineer, a recent grad, or a complete beginner, a key question to ask yourself is what career trajectories are closest to you in parameter space.
First think about what kind of data scientist they want to be. The reason this is crucial is that data science isn’t a single, well-defined field, and companies don’t hire generic, jack-of-all-trades “data scientists”, but rather individuals with very specialized skill sets. Think instead about the kind of value you want to help companies build, and get good at delivering that value. That, more than anything else, is the best way to get in the door.
If you’re doing all of the standard “I want to become a data scientist” things, then this means you shouldn’t expect to land your dream job. The market is currently full of junior talent, and as a result, the median aspiring data scientist is unlikely to get much traction. So if you want to avoid the median outcome, why do median things? The problem is, most people don’t think this way when they embark on their data science journeys.
You want to learn data science on your own. Then, set explicit learning goals. Aim for short-term goals, which can be scoped out more realistically. Commit to your goals publicly. If you can, reach out to an industry professional, and ask them if you can keep them in the loop on your latest work with a weekly newsletter. This is an easy way to give yourself some extra motivation to make sure you have something to show for each week’s work, and doubles as a great way to grow your professional network.