Data scientists, stop doing what everyone else is doing to get hired

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Everyone agrees that when it comes to investing, if you’re doing what everyone else is doing, you’re unlikely to see any returns.

What’s weird, though, is that people, including data scientists, fail to apply this same reasoning when it comes to investing in themselves.

Self-improvement bubble

Suppose you want to get hired as a data scientist. 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 that most people don’t think this way when they embark on their data science journeys. I’ve spoken to literally hundreds of aspiring data scientists through my work at SharpestMinds, and about 80 percent of them have roughly the same story to tell:

1. First, they learn the ropes (Python + sklearn + Pandas + maybe some SQL or something).
2. Then, they take a cookie-cutter MOOC (massive open online course) of some sort.
3. They read a few job descriptions and get worried that they don’t have what it takes.
4. They maybe take another MOOC or start applying for jobs.
5. They hear nothing back (or at best, bomb a few interviews).
6. They get frustrated, think about doing a master’s, and/or apply for some more jobs.
7. They come to a decision point: Do I repeat #2 through #7 until something different happens?

If this has happened to you, odds are you’re in a self-improvement bubble too. You’re doing what everyone else is doing but expecting a different outcome.

The very first thing you need to do is stop.

The not-so-obvious things to do

If you want above-average outcomes, you can’t do average things. But to avoid doing average things, you need to know what they are.

Here are some examples.

If you needed to do an MOOC to learn the ropes, that’s fine. But don’t get stuck in the MOOC spiral. MOOCs are, almost by definition, designed for the average person. So, you won’t become an outstanding candidate by doing more of them. Likewise, if you have four or five Jupyter notebooks featuring the same boring sklearn/Pandas/seaborn/Keras stack on your GitHub, do not make another one.

Overall, the rule is: If something seems like an obvious next step because everyone else is doing it, that’s a great thing to not do. And conversely, you need to find the things that no one else is doing, and do them as soon as possible.

What are those things? Based on what I’ve seen, about five come to mind:

1. Replicate papers