Data Scientist is Still the Sexiest Job of the 21st Century

In 2020, the hiring for data scientists grew to 46 percent, an analysis made by LHH, formerly Lee Hecht Harrison, a global provider of talent and leadership development, career transition, and coaching.

With data generating at an all-time high, organizations will need more data scientists to make more sense of data. Big data and analytics are mission-critical for businesses. However, their only challenge is, hiring efficient and skilled data scientists.

IDC estimates worldwide data will rise 61 percent to 175 zettabytes of data by 2025. More so, around 30 percent of the world’s data will need real-time processing.

"Today, more than 5 billion consumers interact with data every day — by 2025, that number will be 6 billion, or 75% of the world's population. In 2025, each connected person will have at least one data interaction every 18 seconds. Many of these interactions are because of the billions of IoT devices connected across the globe, which are expected to create over 90 ZB of data in 2025."

--- The Digitization of the World

Enterprises and industries across the world have started extensively relying on big data insights using it as their guide to conduct and evaluate daily operations. Whereas product designers use insights to develop new products aligned to the needs of their customers. While advertisers use it to decide whether to make the next investment. If you’re still wondering what data can still do, well, it forms the backbone of even the financial industry. Talking of the healthcare sector, techniques such as data visualization have improved patient outcomes without overloading the physicians’ workload.

With data making its presence across multiple industries, you shouldn’t be surprised to see how data science jobs have started ballooning across the globe.

No wonder why Harvard Business Review called data scientists to be the sexiest job of the 21st century.

A data scientist takes the responsibility of gathering, gleaning, analyzing, and preparing data to further use it to predict business outcomes. Despite the growth of data, the world is still short of skilled workers who could actually use data science tools and techniques. Thus, a growing gap between the need of the organization and the capabilities of the worker to fulfill the job criteria. Recent studies also showcased that there are nearly 100,000 data science jobs in India that were yet to be filled in 2020.

The result: Upskilling to the rescue

Data scientists are offered one of the highest salaries in the technology industry. However, before making a career pit stop, you need to first evaluate where your skills lie, weigh the weakness and strengths, check whether the skills you currently have are still valid in the industry. Data science aspirants might hold undergraduate degrees in multiple disciplines like computer science, engineering, liberal arts, or even statistics and mathematics. However, all these might not suffice to become successful in the field.

While talent in the field is booming at a rapid pace, you also need to know that employers are only interested if you have practical skills. Though the market may seem to produce multiple data scientists, not all of them have sufficient data science skills to take up different roles in the industry.

As mentioned, potential employers seek candidates with practical skills and not a jack of all trades. Perhaps this might be one of the reasons why there’s an off-balance with the supply and demand of data science talent.

For you to stay relevant and noticed in the labor market, you need to demonstrate a specialized skill set in data science. You need to make your job role unique. For instance, find nuances that make a data scientist different from a data analyst or a data engineer. On the contrary, these job roles are not interchangeable. Therefore, you need to be sure which role you’re more interested in.

More so, a data scientist uses advanced data techniques and machine learning techniques like decision trees, clustering, and neural networks to predict business insights. Besides basic coding skills, a data science professional needs to know programming languages like Python, R, Java, Scala, and Julia. Having additional skillsets in machine learning and deep learning is an added advantage to the specialist.

Although data science and machine learning theories and concepts are not something new to the business market today, applying them for business decisions is relatively new. As a result, the field hosts multiple job opportunities not just for tech professionals but for non-tech professionals too.

With the digital revolution being the forerunner, upskilling using professional certifications such as data science certifications and artificial intelligence certifications have become crucial.

Conclusion

The supply of qualified data scientists with machine learning skills is considerably still low in the tech market. Therefore, becoming a data scientist today might act as a golden opportunity for those already in the field of statistics, computer science, engineering, and other related domains.