Overcoming the Talent Shortage in Data Science and Data Engineering in 2023

Overcoming the Talent Shortage in Data Science and Data Engineering in 2023

The Data science skills gap has been a real concern to many organizations for quite a long time, making it unable for them to hire the right data science talent and drive their critical business decisions. But, what is the major issue that causes this talent shortage?

While many data modelers can analyze conceptual or logical data modeling, the real struggle lies in finding those who can collect data, prepare, cleanse and organize the models to scale production. This is why organizations opt for candidates with the best data science education so that they know how to mitigate the real-time challenges in the field.

In this article, let us discuss the talent shortage in Data Science and the best way to overcome the data science skill gap for the best business decisions in 2023. 

Data Science skill gap: The real challenge

Many people still look for theoretical and mathematical concepts of data science but don’t focus on the practical expertise needed to put this into reality. This could be because even certain data science education providers convey that the role of a data scientist is to advance data science knowledge, but not to train the aspirants for a worthy profession. 

While this is also important, we need a balance between both these ends to excel in the field. Without appropriate data science skill development, you never learn how to deploy data science and modeling effectively to the applications and fail to enrich the business decisions.

Data science talent shortage leaves organizations unpredictable

The job listing platforms like Glassdoor, Indeed, and LinkedIn have numerous listings for data scientists who qualify for data engineering certifications. There are over 36000 data scientist job listings on Indeed.com. Based on these job postings, the average salary of data scientists has come to 100,000 USD per the US. Bureau of Labor Statistics.

In the data compilation of Quanthub, it is evident that the data scientists’ shortage of about 2,50,000 has hit the year 2020, concerning the data science job postings and the searches for relevant terms by the aspirants.

Based on the State of the CIO survey in 2021, Data science and analytics skills bagged the top position in the hiring list preferred by the CIOs, while almost 19% of them expressed the challenges in sourcing suitable analytics and data science talents.

The precedence of automation over data science talent

Companies that belong to various industries make efforts to reinvent the strategies to conduct their business with the migration of data workloads. Automation is a way businesses can use advanced tools to ease mundane tasks and save hours of their work. 

While data science talent aids the facilitation of transformation and migration projects, a certain time is spent carrying on repetitive tasks such as data updates, organizing, and management. These tasks had a great impact on the satisfaction of data engineers, who had to spend hours doing repetitive data preparation manually and fixing errors. 

While digital transformation and cloud data migration are imperative, the talent required to drive these processes is scarce. The solution that keeps the businesses forward would be the automation of operations, data integration, orchestration, and data management. With new technologies and tools, automation helps to reduce the time and resources required for business processes.

Let’s consider the example of a data analyst who uses automated data analytics that helps them flag variables within a dataset. With automated analytics systems, the business can build suggestions with the picture of a final statistical model. This can save the time and effort they need to re-operate with a study to assess the various sets of data that are manually chosen and transformed.

Automation has a simple result in terms of opportunity cost versus the value of the investment. Automation can let engineers free from cumbersome tasks and let them contribute to the initiatives at an improved level with high business value. It makes enterprises highly capable of managing numerous data sources and workloads while paving the way to free up time and resources for data science professionals. 

Nevertheless, automation flips the focus of the talented professionals from cyclic data chores to helping businesses to focus on successful and value-rich data initiatives.

Overcoming talent shortage through Data Science Education

Every organization aims at leveraging massive gains with the power of data science. They wish to use their team of data scientists who can develop algorithms that help in troubleshooting potential issues, predict the future, provide recommendations, etc.

But unfortunately, many organizations are stuck with no way to find qualified data science professionals to grow data-driven decisions that reap profits. 

As per the US Bureau of Labor Statistics, the data science jobs will hit a growth of 28% by 2026 from 2020, which makes 11.5 million new roles in the industry. However, despite this increased demand for data scientist positions, there is a lack of supply of eligible data science professionals.

Data science is all about being capable of recognizing the issues that need solutions and the methods to add value and generate profits for the business, hence organizations seriously hunt for the best candidates with industry-specific data science skills.

Despite the need for organizations to hire data scientists and use their expertise to revolutionize the decision-making process, many data science aspirants are not aware of how to get hired through the right development of data science skills. Unlike today, the Data science and data engineering certifications were not prominent earlier, but with the advent of big data and the emergence of data analytics, data science has become a crucial field of study. 

Automation has made tasks streamlined, paving the way for data scientists to focus on more refined and talent-specific processes. Data science certifications are necessary to augment these processes and let organizations identify you as a qualified professional through data science education.


Data science certifications from premier institutes such as MIT, Stanford, USDSI, Harvard, etc., are becoming popular, urging the existing workforce as well as the data science aspirants to take up data science education for the right set of skulls. 

Through these career-oriented certifications, you get better equipped to be capable of analyzing data sets. Also, data science courses help you learn how to deploy new tools and technologies to understand and rectify the anomalies or outliers in the data.

Mon, 09/19/2022 - 06:00