What has changed in the data science landscape, and what are the challenges of the 2024 data science job market?
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What does the world of data science look like in 2024? To answer this question and tell your fortune, we must go back several years into history. We’ll explore how we came from the boom years of 2020 to the more specialized and nuanced fields that are about to mark 2024.

Rewind to 2020

In 2020, the world was in the grip of the COVID-19 pandemic, and industries were facing unprecedented challenges. This really propelled the tech industry, though, as many things went online rather than in person. More specifically, data science was on the rise, with a 50% increase in demand across various industries and markets. Healthcare, tech, media, and financial services were particularly hungry for data science talent and went on a hiring spree.

2022 and 2023 Layoffs

The high demand for data scientists didn’t last that long. As the pandemic subsided during 2022 & 2023, this saw a dramatic shift in the data science market: a hiring spree became a laying-off spree.
Source: https://techcrunch.com/2024/01/25/tech-layoffs-2023-list/
Big tech companies cut down their job postings by 90%. It was a tough market for both the entry-level data scientist and the experienced scientist. Over this 2-year period, we saw over 500,000 layoffs across all of the jobs in the tech industry, with over 30% of those layoffs in the engineering and data science roles.

Specialization & AI Era

These two years didn’t bring only layoffs. There’s also a significant change in the rise of specialization. The general data scientist’s role began giving way to more focused positions like machine learning engineers and data engineers. There was less emphasis on the data scientists that could do the end-to-end work.
And, of course, let’s not forget the impact of AI. Specifically, OpenAI tools like ChatGPT have made AI more accessible and data science work more efficient and automated.

Data Science Landscape in 2024

Though the overall job opportunities have decreased, the market is finally stabilizing.
Source: https://www.interviewquery.com/p/september-data-science-job-market
There is a particular demand for experienced professionals in specialized roles.
Your ability to code is as important as ever. This is particularly important for jobs in machine learning engineering, where you’ll have to use code in these data science techniques.
There’s more consolidation in the programming languages a data scientist would use. This is often dominated by Python.
Source: https://blog.jetbrains.com/pycharm/2023/10/future-of-data-science/
And SQL will be there forever. On the other hand, some languages, such as R, SAS, and SAP, are declining in popularity.
So, if you’re trying to become a data scientist from scratch and decide what language to learn, Python and SQL will always be there and will always be the dominant languages.
Interestingly, some jobs, such as data analysts and business analysts, are benefiting from the rise of low-code and no-code tools that were made popular by the rise in AI, specifically plugins that ChatGPT can use to automate a lot of data science work.
The data science market is now more bifurcated than it was before. We see clear distinctions between jobs like business analysts, AI/ML engineers, and data engineers. Then it’s trifurcated?

Challenges for Data Scientists in 2024

The challenge for data scientists is to demonstrate their value in ROI.
Source: https://www.linkedin.com/pulse/value-roi-fiona-anderson/
The initial hype is settling, and companies are looking for results. Data scientists will need to prove their worth here.
They’ll need to specialize in their skills, whether it be ML engineering,
https://www.datacamp.com/blog/machine-learning-engineer-salaries-in-2023
data engineering,
Source: https://medium.com/@viduhewage02/roles-in-database-management-and-development-3072d3d221b0
or data analytics.
Source: https://www.upwork.com/en-gb/services/product/development-it-an-expert-in-power-bi-tableau-and-microsoft-excel-for-data-analytics-1692392828735832064
They’ll need to adapt to the new powerful tools being developed, like Google’s Gemini, Galactica, and ChatGPT.

Conclusion

Since the beginning of time, data science has been a constantly changing field. It has changed in the last several years and will continue to do so in 2024. Data scientists must adapt, evolve with it, and rise to new challenges and opportunities.
The main challenges are specialization and keeping up with the latest AI tools developments. And, as was always the case, you have to demonstrate your value to the potential employer.