AI is changing the skills needed of tomorrow’s data scientists

There’s a global shortage of data scientists and it’s going to get worse before it’s going to get better.

As the demand for data scientists increases, the supply of talent in the US cannot keep up. According to the McKinsey Global Institute, the U.S. economy could short as many as 250,000 data scientists by 2024. Countries like Malaysia are building national programs to fill the gap as they seek to become a global hub of data science talent. The United States needs to apply more resources in order to grow the number of US-trained data scientists and maintain a global leadership position in data science.

As data becomes more ubiquitous in every job role and in every organization, data scientists will only grow in importance. Today’s students will be the first data-native employees of the future, and it’s critical that they understand data science, how data science is changing and how data science solves real world problems.

The potential for the next wave a data scientists is huge but for them to be successful, education must evolve.

Automation and artificial intelligence (AI) are already changing workforce requirements of data scientists. Traditionally, working with data has been complex and highly manual work, requiring specialized technical skills. Today, we’re in the beginning stages of applying machine learning and artificial intelligence to the operation of complex IT systems. We’re seeing how algorithms can improve and streamline security, network management and now data platforms.

AI is changing the nuts and bolts of data management, alleviating data teams from a lot of tedious, manual dirty work so that they can focus their time on creating business outcomes and allowing data scientist to work at a speed and scale that is impossible today. The data scientist of tomorrow must be prepared to work with the AI revolution, optimizing processes without losing the human ability to think creatively and apply data-driven insights to real-world problems.

The next generation of data scientists will be even more necessary for helping to apply models and algorithms to problems and processes across the enterprise.