The problem with data science job postings

Why are job postings so confusing (in that they fail to clearly specify the skills they expect from a candidate), or so outrageously over-reaching (“looking for a machine learning engineer with 10 years’ experience in deep learning…”)?There are many reasons.

For one, companies make hiring decisions based on a candidate’s (perceived) ability to solve a real problem that they actually have.

Because there are many ways to solve any given data science problem, it can be hard to narrow down the job description to a specific set of technical skills or libraries.

That’s why it usually makes sense to put in an application for a company if you think you can solve the problems they have, even if you don’t know the specific tools they ask for.

Another possible reason is that many companies don’t actually know what they want — especially companies with relatively new data science teams — either because the early stage of their data science effort forces everyone to be a jack of all trades, or because they lack the expertise they need to even know what problems they have, and who can help solve them.

If you come across an oddly non-specific posting, it’s worth taking the time to figure out which bucket it belongs to, since the former can be a great experience builder, whereas the latter can be a recipe for disaster.

But perhaps the most important reason is that job postings are often written by recruiters, who are not remotely technical.

This has the unfortunate side-effect of resulting in occasionally incoherent asks (“Must have 10+ years’ experience with deep learning…”, “…including natural language toolkits, such as nltk, gensim, OpenCV, …”) or asks that no human being could possibly satisfy.

The net result of this job qualifications circus is that I regularly get questions from our mentees about whether they’re qualified for an opening, despite having read all the information on the internet about that position.

So let’s tie this one up in a bow with a few easy rules:If you have no prior experience, don’t bother applying to jobs that ask for more than 2 years of it.

When they say “or equivalent experience”, they mean, “or about 1.

5X that much experience working in a MSc or a PhD where you worked on something at least related to this”.

If you meet 50% of the requirements, that might be enough.

If you meet 70%, you’re good to go.

If you meet 100%, there’s a good chance you’re overqualified.

Companies *usually* care less about the languages you know than the problems you can solve.

If they say Pytorch and you only know TensorFlow, you’re probably going to be ok (unless they stress the Pytorch part explicitly).

Don’t ignore lines like, “you should be detail-oriented and goal-driven, and thrive under pressure”.

They sound like generic, cookie-cutter statements — and sometimes they are — but they’re usually written in a genuine attempt to tell you what kind of environment you’ll be getting yourself into.

At the very least, you should use these as hints about what aspects of your personality you should emphasize to establish report with your interviewers.

None of these rules are universally applicable, of course: the odd company will insist on hiring only candidates who meet all their stated requirements, and others will be particularly interested in people who know framework X, and will disregard people who can solve similar problems, but with different tools.

But because there’s no way to know that from job descriptions alone (unless they’re explicit about it), your best bet is almost always to bet on yourself, and throw your hat in the ring.


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