ZACHOVERFLOW

Why Is Landing A Data Science Role So Hard?

A six-figure job that was once a sure bet is now being oversaturated. Why is landing a data science role so hard?

ZachOverflow is a recurring column in which I attempt to answer one frequently asked data science question thoroughly and honestly. No oversaturated topics. No listicles. No clickbait. Just my (mostly) unfiltered responses based on professional experience, technical exposure and, yes, the occasional unsubstantiated opinion.

When I tried to secure a data science (or more accurately data engineering) role in the spring of 2021 I learned something unsettling: I had been lied to.

Armed with a master’s degree and technical competence the minimum requirements for most data-oriented roles, professors, institutions and well-meaning family members assured me that finding a job would be easy.

In my essay “Did I Make A Mistake Going Into Data Science?” I touched on the disconnect between those currently in the field, educators and those trying to “get into” roles on the data science spectrum.

Prior to that piece I wrote another article about literally giving up the job search for three months before readjusting my strategy.

If you’re feeling similarly discouraged by the lack of recruiter follow ups and interviews, it’s because no one told you, truly, how difficult it is to enter this field.

That’s why you’ll hear tales of woe on data science or tech forums but rarely see this mentioned in promotional materials for universities or online courses.

The way data science is discussed positions it as a “sure bet” for candidates not only looking for a stable income, but to make a significant jump in earnings. And as companies race to create intuitive and borderline sentient AI, data-oriented roles seem (for now) to be a safe hedge against the automation wave likely to impact a swath of white collar jobs.

Again, this was a sure bet. This is why you made the leap and went back to school, enrolled in a micro degree program or sacrificed sleep for two years teaching yourself how to code and develop ML models.

But the same factors that make data science a “sure bet” for those looking to transition careers, some of which aren’t even in a STEM field (I have my hand raised), also make it very attractive for a certain group of people:

Those with way more experience than you.

I’m not just talking about someone with a master’s degree. I mean someone who works in a field that already uses data science skills without the title. For instance, a professor of mine began his career in academia and medicine where he already used Python and advanced math to conduct research. He leveraged that experience and landed a cushy job as a senior data scientist at a large credit card company.

Oh, and like 80% of data science candidates, he had a postgraduate degree.

For someone like this, transitioning to data science wasn’t just a lateral move. It was a no-brainer.

These are the individuals who can actually snatch up the six-figure jobs with “no experience” because they have a wealth of transferable skills.

Thanks to the inconsistency of job titles, these folks are also your competition.

This is especially true after significant layoffs in 2022 that cut loose entry-level and mid-level engineering talent across “tech”, impacting everyone from early stage startups to FAANG companies that “over hired” post-pandemic. After the FAANG engineers enjoy their paid time off and finish counting the zeros in their severance packages, they’re back in the job market.

And since these might not be the most senior individuals, they’re gunning for the same roles you are. Except they have “Amazon” or “Apple” or “Twitter” (or maybe X) on their resumes.

For a recent data science graduate, even a former FAANG junior engineer isn’t a fair fight.

At the same time as companies are downsizing, they’re also rethinking remote work arrangements, with both tech and non-tech organizations issuing mandates for workers to return to the office. While this might help narrow your competition to your geographic area or the area where you’re willing to move, this trend has also significantly increased the competition for remote tech roles.

Pardon the interruption: Trying to land your first data science job? Create a job-worthy data portfolio. Learn how with my free project guide.

Despite the macroeconomic and societal factors I outlined, when I finally got recruiters and hiring managers to return my calls, I faced a hurdle: Getting people to take me, a qualified candidate but new graduate, to take me seriously.

Generally, I’ve been fortunate to have cordial exchanges with recruiters. However, one phone call provided a reality check and made me seriously doubt my skills. I had a call with a recruiter for a large pet goods company. We had a pleasant conversation with me softly pitching myself as a fit for the company. Then, when it came time to discuss role responsibilities and compensation, he dropped a bomb.

Due to my new grad status, they could bring me on, but only as an intern, paid a (shockingly low) hourly wage. If I could “prove myself” I could become a full team member.

I declined. But I also felt uneasy. If this company could write me off as a qualified candidate, why would anyone else see me as anything more than intern status?

I quickly learned that if you’re going to jump from student to professional you need to not only confidently present yourself, but you also need to confidently value yourself, salary-wise. This is the advice I give to interns and those who reach out to me. Your sense of self-valuation is one of the few factors you can control in a tough job market.

To me, one of the most telling indicators that data science is a tough field to enter is the fact that hopefuls don’t ask: “How do you get a data science job?” They ask: “How do you break into data science?”

The first implies attainability, almost inevitability. The second, more commonly applied to those chasing entertainment roles, makes it sound like you need something besides skill. A lucky break, if you will.

While it’s not a sure thing that you’ll land your dream data role (though I hope you do), thinking of the process as anything other than applying to and being offered a job only makes the prospect more daunting than it needs to be.

My purpose in writing this and addressing you isn’t to discourage you from pursuing a role within the data science field. It’s not a “reality check” to give you a thick skin as you open your 200th auto-reject email.

Instead, it’s my attempt to put into words the underlying factors I’ve encountered and observed that make the data science job market intimidating, difficult to navigate and discouraging for qualified, passionate candidates like yourself.

So much has been written about how to land a data science job that I wanted to share the counter-argument to the “5 Tips To Land Your Dream Data Science Role”-type pieces that overwhelm your work-oriented social feeds.

In my writing and dialogue with data science hopefuls I try to be truthful about both the nature of the job and the hurdles you’ll face.

To that end, I’ve curated a list of career-oriented pieces that can help you distinguish yourself in a crowded and, yes, hard job search.