Hybrid Data Scientists: Key to Leading and Optimizing Analytics Efforts

Hybrid Data Scientists: Key to Leading and Optimizing Analytics Efforts

The hybrid data scientist is one whose work reflects many different data science roles. Hybrid data scientists, compared to data scientists who play a single, narrower role, possess deeper knowledge in particular data science skills such as machine learning, managing unstructured data and optimization. Organizations can leverage their deeper knowledge to improve their data science efforts.

The success of a data science program rests on the skills of the data scientists doing the work. Because different types of data scientists have unique skills, it's important that you get data scientists who possess the skills you need to address the problems you want to solve. While our earlier reporting focused on understanding the difference among four types of data scientists (Business Manager, Developer, Creative and Researcher), we want to now understand the data professional who self-identifies as multiple types of data scientists, the hybrid data scientist. Do these hybrid data scientists possess more skills than their counterparts?

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Based on over 600 responses to our survey, data professionals self-identified as different types of data scientists. Most of the respondents (49%) said that the work they do is best described as falling into one data science role (see Figure 1).

Nearly all of the remaining data professionals self-identified as some form of a hybrid data scientist. Nearly a third of the respondents (32%) indicated the work they do with data is best described as two different roles. A few of the respondents indicated that the work they do falls into three (13%) and four (4%) data science roles.

There are many different types of data scientists. As you can see in Figure 2, the most common type of data scientist was the Researcher (22%), followed by Business Manager (14%) and Developer (9%). The next most common data scientists were the Researcher/Developer (8%) and Researcher/Creative (8%).

Some of the data professionals who were specific types of hybrid data scientist combinations appeared to be more proficient in some data science skills compared to data scientists in single roles.

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