Skip to main content

The business and processes of data analytics require a high level of expertise at the controls. Data scientists are in demand for this reason. However, getting the right person for the job of constructing something meaningful out of the reams of raw information gathered every second by your organization is a little more complicated than you think. Even if this scientist has an understanding of predictive modeling software, that's no guarantee they have the talent and skills necessary to complete the job and give something of value to the company. With the definition of a data scientist still in flux, companies should take some reasonable guidelines in finding an ideal candidate for the job.

The internal skills
At the heart of a good data scientist is a strong computer science and mathematics background. This starts at the educational level, according to recruiting firm Burtch Works. While it's possible to get a bachelor-level candidate that can do really well, the best in class tend to have graduate-level credentials, up to receiving a Ph.D in a related field such as statistics. This is because the amount of analytical know-how that ties into data science is vast.

"Certain programming languages are necessary, namely Python, R and SQL databases."

Along with the education comes experience in key technical fields. Certain programming languages are necessary, namely Python, R and SQL databases. Understanding the Hadoop platform is a plus in any book. As mentioned previously, having expertise in a specific system such as IBM Predictive Analytics – which powers Cognos BI – makes a great data scientist stand out in many ways. Finally, it plays to many businesses' interests to look for someone who can handle unstructured data such as audio or social media.

The external acumen
There are non-technical skills that businesses should consider when selecting a data scientist as well. Business skills are an absolute must, according to Data Science Central. Many potential candidates often get bogged down by the academic background. As such, they lack the capacity to explain the value of specific data sets to businesses that need this information. An expert in this field will understand the significance and weight of terms such as return on investment.

Creativity is also a major strength for many of the best data scientists. There's a lot of intangible meanings that come with that requirement. With this in mind, a good candidate should be curious about the data they handle, coming up with new ideas regularly. They should have excellent storytelling abilities, presenting a distinct view of what businesses have with their data. Combined with practical business, analytical and technical skills, these are the makings a great data scientist.