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I once spoke with a guy who bragged that, armed only with some free LinkedIn courses and an outdated college Intro to SQL course, he’d managed to bag a six-figure job in data science. Nowadays, most people struggling to get a good data science job will agree that’s unlikely to happen. Does that mean the data science job category is a popped bubble – or worse, that it hasn’t yet burst, but is about to?
In short, no. What’s happened is that data science used to be an undersaturated field, easy to get into if you used the right keywords on your resume. Nowadays, employers are a little more discerning and often have specific skill sets in mind that they’re looking for.
The bootcamps, free courses, and ‘Hello World’ projects don’t cut it anymore. You need to prove specific expertise and nail your data science interview, not just drop buzzwords. Not only that, but the shine of “data scientist” has worn off a little. For a long time, it was the sexiest job out there. Now? Other fields, like AI and machine learning, are just a bit sexier.
That all being said, there are still more openings in data science than there are applicants, and reliable indicators say the field is growing, not shrinking.
Not convinced? Let’s look at the data.
The Big Picture
Over the course of this article, I’ll drill down into multiple graphs, charts, figures, and percentages. But let’s start with just one percentage from one outstandingly reputable source: The Bureau of Labor Statistics.
The BLS predicts that there will be a 35 percent change in employment from 2022 to 2032 for data scientists. In short, in 2032, there will be about a third more jobs in data science than there were in 2022. For comparison, the average growth rate for all jobs is 3 percent. Keep that number in mind as you go through the rest of this article.
The BLS does not think that data science is a bubble waiting to burst.
The Layoffs
Now we can start getting into a bit of the nitty gritty. The first signs people point to as signs of a popped or impending bubble pop are the mass layoffs in data science.
It’s true that the numbers don’t look good. Starting in 2022 and continuing through 2024, the tech sector in general experienced 430k layoffs. It’s difficult to tease out data science-specific data from those numbers, but the best guesses are that around 30 percent of those were in data science and engineering.
Source: https://techcrunch.com/2024/04/05/tech-layoffs-2023-list/
However, that’s not a burst bubble of data science. It’s a little smaller in scope than that – it’s a pandemic bubble popping. In 2020, as more people stayed home, profits rose, and money was cheap, FAANG and FAANG-adjacent companies scooped up record numbers of tech workers, only to lay many of them off just a few years later.
If you zoom out and look at the broader picture of hirings and layoffs, you’ll be able to see that the post-pandemic slump is a dip in an overall rising line, which is even now beginning to recover:
Source: https://www.statista.com/
You can clearly see the huge dip in tech layoffs during 2020 as the market tightened, and then the huge spike starting in Q1 of 2022 as layoffs began. Now, in 2024, the number of layoffs is smaller than in 2023.
The Job Openings
Another scary stat often touted is that FAANG companies shuttered their job openings by 90% or more. Again, this is most in reaction to a widely high number of job openings during the pandemic.
That being said, job openings in the tech sector are still lower than they were pre-pandemic. Below, you can see an adjusted chart showing demand for tech jobs relative to February 2020. It’s clear to see that the tech sector took a blow it’s not recovering from any time soon.
Source: https://www.hiringlab.org/2024/02/20/labor-market-update-tech-jobs-below-pre-pandemic-levels/
However, let’s look a little closer at some real numbers. Looking at the chart below, while job openings are indubitably down from their 2022 peak, the overall number of openings is actually increasing – up 32.4% from the lowest point.
Source: https://www.trueup.io/job-trend
The Narrative
If you look at any labor and news reports online, you’ll see there’s a bit of an anti-remote, anti-tech backlash happening at the moment. Meta, Google, and other FAANG companies, spooked by the bargaining power that employees enjoyed during the pandemic heights, are now pushing for return-to-office mandates (data science jobs and other tech jobs are often remote) and laying off large quantities of employees somewhat unnecessarily, judging by their revenue and profit reports.
Just to give one example, Google’s parent company Alphabet laid off over 12,000 employees over the course of 2023 despite growth across its ad, cloud, and services divisions.
This is just one facet with which to examine the data, but part of the reason companies are doing these layoffs is more to do with making the board happy rather than any decreased need for data scientists.
The Demand
I find that people believing we’re in a data science bubble are most often those who don’t really know what data scientists do. Think of that BLS stat and ask yourself: why does this well-informed government agency believe that there’s strong growth in this sector?
It’s because the need for data scientists cannot go away. While the names might be changed – AI expert or ML Cloud Specialist rather than Data Scientist – the skills and tasks that data scientists perform can’t be outsourced, dropped, decreased, or automated.
For example, predictive models are essential for businesses to forecast sales, predict customer behavior, manage inventory, and anticipate market trends. This enables companies to make informed decisions, plan strategically for the future, and maintain competitive advantages.
In the financial sector, data science plays a crucial role in identifying suspicious activities, preventing fraud, and mitigating risks. Advanced algorithms analyze transaction patterns to detect anomalies that may indicate fraud, helping protect businesses and consumers alike.
NLP enables machines to understand and interpret human language, powering applications like chatbots, sentiment analysis, and language translation services. This is critical for improving customer service, analyzing social media sentiment, and facilitating global communication.
I could list dozens more examples demonstrating that data science is not a fad, and data scientists will always be in demand.
Why Does It Feel Like We’re In A Bubble?
Revisiting my anecdote from earlier, part of the reason it feels like we’re in a bubble that is either popping or about to pop is the perception of data science as a career.
Back in 2011, Harvard Business Review famously called it the sexiest job of the decade. In the intervening years, companies hired more “data scientists” than they knew what to do with, often unsure about what data scientists actually did.
Now, a decade and a half later, the field is a little wiser. Employers understand that data science is a broad field, and are more interested in hiring machine learning specialists, data pipeline engineers, cloud engineers, statisticians, and other specialties that broadly fall under the data science hat but are more specialized.
This also helps explain why this idea of walking into six figure job straight out of a bachelor’s degree used to be the case – since employers didn’t know better – but now is impossible to do. The lack of “easy” data science jobs makes it feel like the market is tighter. It’s not; data shows job openings are still high and demand is still greater than the graduates coming out with appropriate degrees. But employers are more discerning and unwilling to take a chance on untried college grads with no demonstrated experience.
The Need For Data Science Has Not Decreased Or Been Replaced
Finally, you can take a look at the tasks that data scientists do and ask yourself what companies would do without those tasks getting done.
If you don’t know much about data science, you might guess that companies can simply “automate” this work, or even go without. But if you know anything about the actual tasks data scientists do, you understand that the job is, currently, irreplaceable.
Think of how things were in the 2010s: that guy I talked about, with just a basic understanding of data tools, catapulted himself into a lucrative career. Things aren’t like that anymore, but this recalibration isn’t a sign of a bursting bubble as some believe. Instead, it’s the field of data science maturing. The entry-level data science field may be oversaturated, but for those with specialized skills, deep knowledge, and practical experience, the field is wide open.
Furthermore, this narrative of a “bubble” is fueled by a misunderstanding of what a bubble actually represents. A bubble occurs when the value of something (in this case, a career sector) is driven by speculation rather than actual intrinsic worth. However, as we covered, the value proposition of data science is tangible and measurable. Companies need data scientists, plain and simple. There’s no speculation there.
There’s also a lot of media sensationalism surrounding the layoffs in big tech. While these layoffs are significant, they reflect broader market forces rather than a fundamental flaw in the data science discipline. Don’t get caught up in the headlines.
Finally, it’s also worth noting that the perception of a bubble may stem from how data science itself is changing. As the field matures, the differentiation between roles becomes more pronounced. Job titles like data engineering, data analysis, business intelligence, machine learning engineering, and data science are more specific, and require a more niche skill set. This evolution can make the data science job market appear more volatile than it is, but in reality, companies just have a better understanding of their data science needs and can recruit for their specialities.
Final Thoughts
If you want a job in data science, go for it. There’s very little chance we’re actually in a bubble. The best thing you can do is, as I’ve indicated, pick your specialty and develop your skills in that area. Data science is a broad field, spilling over into different industries, languages, job titles, responsibilities, and seniorities. Select a specialty, train the skills, prep for the interview, and secure the job.
Nate Rosidi is a data scientist and in product strategy. He’s also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.
