While the number of job openings for data scientists is on the rise, there’s no shortage of vagueness on what the role actually does. Below we aim to fill in the blanks on a position that’s grown by 650 percent since 2012.

The data scientist role typically focuses on analyzing and interpreting data to assist a business in decision making. Some required skills for data scientists are expertise in data visualization, artificial intelligence (AI) and machine learning, as well as experience working with unstructured data. More technical skills such as coding and programming may also be required.

According to a recent KDNuggets article on data scientists, “88 percent have at least a Master’s degree and 46 percent have PhDs.” Increasingly, communications and leadership skills are also emerging requirements for success, as companies look for champions to help drive the adoption of data analytics across the entire organization.

When it comes to the different roles of data scientists in the workforce today, a recent HBR article offered some key definitions outlined below:

1. Data science for humans: Analytics are often aimed at delivering insights to people, including business leaders, managers, designers, and engineers. Data scientists in this realm design and implement metrics, interpret data, create dashboards, and draw conclusions. Business intelligence comes in the form of reports, analysis, and predictions, which are used to make smarter decisions.

2. Data science for machines: Ever wonder where algorithms go? Data science for machines means producing the data, models, and algorithms that systems use to perform analytics and make predictions. This can include recommendation systems, automated proactive customer engagement, and solutions to optimize operations.

While smaller organizations may have only one data scientist on staff, larger companies may quickly grow the team from one to many. More defined roles can also emerge as digital transformation progresses, covering areas like data infrastructure, data engineering, data quality, and governance, to name a few.

Hiring data scientists is not an easy task. The size of your company and the current state of your data operation are both factors that need to be considered when investing in data scientists. According to the HBR article, companies just embarking on a digital transformation journey should hire data scientists with a broader skill set who can take on a larger role for the company. On the other hand, larger organizations further along in the adoption of analytics can hire people with more specialized skills.

Just when you finally have it all figured out, some experts predict that the role of data scientist will simply go away. Well, not really.

According to this Forbes contributed article, there will be no data scientists by 2029. Instead, the job will evolve into a common skill that many, if not all, workers will need. Data scientist titles will make way for more specific roles like AI Engineer or Machine Learning Engineer with data science becoming a common skill or designation, much like computer science or an MBA today.

While titles may change and jobs may evolve, data science is here to stay. Its impact on the future of business cannot be underestimated. Understanding the role and impact of data scientists and the business-critical skills sets they bring to your workforce is an essential requirement for companies to become leaders in the digital age.