Data science career: is it still lucrative in 2021?

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Data science career: is it still lucrative in 2021?

Current status and prospects of the data science job market

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The Data science job market has been booming in the last decade. Data scientists were one of the most in-demand technical experts in the IT industry. Research shows that the average number of job postings on famous job portals increased by about 30% year over year during 2013–2019, and overall, the data science job market showed 344% growth during the same period [1]. In fact, during the last decade, everybody agreed with how Harvard Business Review [2] saw the data science career: the sexiest job of the 21st century!

However, the growth of the data science job market slowed down in 2020, and that made many data science talents wondering about the prospects of this job market. According to the research, the growth of the data science job market has slowed down by 15% during 2020, comparing to the previous year. On top of that, the job market of data engineering, a sister field of data science, has grown by a faster rate during 2020 [3]. These statistics might be why some experts in the big data industry are now considering that data engineering will take data science as the next hot career for data practitioners [4]. Some people even claim that the data science industry is a bubble that has burst or will burst soon [5].

This article will discuss the current status of the data science job market and its prospect. We try to bring you five reasons why the data science job market might still be a lucrative one:

  1. Data analytics is a fast-growing global market

According to various market research firms, the worldwide revenue of the artificial intelligence (AI) software market is projected to hit over 126 Billion dollars by 2026. In fact, experts believe that the global data analytics and artificial intelligence market will consistently grow by 30%-40% during the next five years [6]. This level of growth in companies’ revenue means that the data analytics and AI software companies have to expand their teams to catch up with the market’s overall growth, which means there are more job opportunities for the talents.

2. The pandemic drives the data science job market slowdown, and it is probably temporary

The slowdown of the rate that companies hire data science talents for the projects was observed during 2020 and led many people to believe that data science is a burst bubble. However, according to a survey conducted by Gartner [7], 47% of corporate decision-makers have not changed their plans to invest in artificial intelligence and machine learning technologies, and 30% of them even are planning to increase their investments.

Also, Gartner believes that machine learning and its related fields like deep learning, computer vision, natural language processing are beyond their peak of inflated expectations [7], which is excellent news for data scientists. Being beyond the peak of the hype curve means companies are starting to make realistic plans about machine learning and data science. Soon enough, data science may become a standard and profession like software engineering, which is always in demand.

3. Growth of other data professions is advantageous for data science

There is this narrative in the online media that data engineering will take over data science as a profession [4]. However, one might argue that other data-related fields’ growth is advantageous for the data science job market. Anybody who has worked on a big data project knows that building data-driven software is a team effort. You may need to bring on a team of software engineers, data engineers, Cloud & DevOps specialists, and data scientists together to build AI-based software. That is why bringing more experts or automation tools to a software team working on AI products will help data scientists focus on doing their actual job, which is doing data analytics, building and optimizing high-performance machine learning models to communicate actionable insights with business stakeholders.

4. Post-pandemic recovery requires a dedicated data science team

Companies are forced to adopt more big data analytics and machine learning technologies to grow in the post-pandemic economy. With the trends of AI-driven automation, companies have to hire AI and data science experts to train the machine learning models for their enterprise software. Also, with the increasing trends of data-driven strategy, companies have an incentive to develop more analytics dashboards and tools that facilitate decision-making. Some experts might think that these tasks can be automated with the existing software-as-a-service and cloud solution. But, it is improbable that companies can find a way to eliminate all the hard work required to derive meaningful business insights from raw data or train high-performance machine learning models. Therefore, the post-pandemic growth will boost the demand for all data practitioners, including data scientists and data engineers.

5. Global corporations still prefer to keep the technology development in-house

Many experts argue that the increasing number of software-as-service and cloud solutions for data science and machine learning will eventually automate the role of data scientists. But, if we look at the AI & machine learning projects done within global corporations, especially Fortune 500 companies, there is a strong tendency to keep the data and the expertise in-house. This may be due to several reasons, such as data security, protection of trade secrets, sophisticated functional requirements, or high levels of performance expectations. Therefore, it is predictable that Fortune 500 companies will stick to the in-house development of customized AI-based software solutions rather than employing off-the-shelf software solutions. This means that there will always be a demand for data scientists and machine learning experts who have a corporate mindset and can work within the scope of large-scale IT projects with these types of companies.


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