These are the main skills a data scientist needs

Candidates looking for a job as a data scientist are currently in high demand. Mark Standen and Martin Pardey of Hays Technology explore what the role entails.

A data scientist must manage large amounts of unstructured data, which is one of the ways the role differs from that of a data analyst.

This data comes from multiple sources and a data scientist will then produce solutions that they can provide to the business. To do this, they use algorithms, artificial intelligence and machine learning, among other methods.

Data scientist roles are expected to be one of the most in-demand tech jobs this year. Organizations are looking for people who will come to extract data and then offer information so that the company can act.

The most useful skills a data scientist can have really depends on the role. We can divide the roles into three main pillars.

Analytic

A solid understanding of mathematics is a must, while a degree or PhD in computer science, statistics, or engineering is strongly preferred.

Data scientists will use analytical tools, so mastering these will be useful. Examples include SAS, Hadoop, Hive, Apache Zeppelin, Jupyter Notebooks, and Pig, among others.

Technical

The ability to use the aforementioned analytical tools will be important. In addition to this, an ideal candidate will be comfortable (or at least proficient) in programming languages ​​such as Python, R, SQL, Perl (5) and C/C++.

This is also where an understanding of artificial intelligence and machine learning will be important when processing data.

Commercial

This pillar is more distinct and, although there is some overlap, requires a different set of skills. A working knowledge of the relevant industry is valuable, as is how the data and information will be used.

While not unimportant in the first two pillars, soft skills take on greater importance here. Candidates will have greater business acumen and an ability to communicate.

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For novice data scientists

As mentioned, having a degree in math or statistics is very advantageous, while an advanced degree in a related field is not a bad thing.

In addition to this, I would recommend having some form of experience in analytics or scientific dissertations, particularly if it involves working with unstructured data.

Employers will be looking for candidates who can code, so learning to write one of the languages ​​listed above would be a good start. The recruiter may want to see evidence of this, which means candidates should be prepared to pitch.

Beyond that, there are some soft skills that future data scientists will have mastered in a previous role, or even during their studies. I would emphasize critical thinking, complex problem solving, risk analysis and teamwork.

How Data Science Has Changed

Until a few years ago, we saw companies hiring a data scientist without a real implementation strategy.

As the tech industry has exploded, we are now seeing that organizations are more informed/well-prepared regarding their data strategy. As a result, they have a much clearer idea of ​​the role a data scientist can play for them.

Of course, this has been accompanied by an improvement in the technology available to these organizations. Most platforms operate in a certain way, but a good data scientist will adapt to advancement. Change is essential to the role.

Through Marc Standen and Martin Pardey

Mark Standen is director of the intelligent automation practice at Hays Enterprise Technology. Martin Pardey is director of Hays Technology in the South East. A version of this article originally appeared on the Hays Technology blog.

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Sean N. Ayres