Non-technical skills required by a data scientist


 If we had to name one technology related aspect, over and above Artificial Intelligence (AI) of course, that has seen the industry abuzz with excitement and progression, I am sure that like me, for many of you the term Data Scientist comes to mind. However, in addition to all the analytical and statistical skills, a full rounded data scientist needs some essential non-technical skills too.

Once thought to be a unicorn of the data industry, the Data Scientist, although still quite scarce, has grown somewhat over the past few years. As a result, a skill that was previously considered a pipe dream, is now slowly starting to become more of a reality that many organisations are trying to capitalise on. In fact, being a Data Scientist was named as the best job in the United States for 2019.

While a skill that is absolutely relevant in this digitally transforming world and thus a role that we tend to get a bit carried away with – as we decipher between the skills set and knowledge base a true data scientist should hold – an industry article I recently came across provided me with a very sharp reminder of the holistic skills and characteristics a truly successful data scientist holds.

The right analytical background and statistical knowledge may form the key foundation on which a data scientist performs, but what value is a data scientist who may be ‘red-hot’ on paper and can create the most amazing and valuable insights, yet is unable to communicate these back to the business in a manner which can be understood and onboarded for real change to take place? All this results in is a bunch of wonderful insights that aren’t actually worth all that much.

It is critically important to understand that for a data scientist to add real value to a business and institute positive and beneficial change, there are a certain set of non-technical related softer skills the expert must attain. Often these skills are overlooked yet provide the ability to elevate the data science role and as identified in this article, can set each data scientist apart from each other and their competition.

So, what do these softer skills consist of? Below is an outline of those identified with my take on each:

  • Intellectual curiosity – this is a softer skill (or what can be considered more of an interest) that not every statistical expert naturally holds. Being curious about certain findings or insights and in some cases, veering away from the ‘by the book’ principle can in fact unlock another whole world of further insights. Being curious about intelligence often leads a data scientist to explore beyond what is required, making them truly valuable to an organisation and its future growth and sustainability.  But possessing this skill in such a statistical dependent role isn’t always easy.
  • Business acumen – data and the insights that can be derived from it can only present value if mapped back to the business objectives or goals. Holding an interest of the business, understanding how it operates, the objectives it has set, and the future growth path are all critical to not only deriving the right insights from data analytics, but to gain value from those insights. Business acumen is therefore a key softer skill that any successful data scientist needs to bed down.
  • Communication skills – as mentioned above, a data scientist can gather all the necessary insights needed to make strong and valuable business decisions – but if the insights cannot be communicated in a way that the business leadership team understands, then the insights are of very little value. The ability to communicate effectively, in time and to the right audience, will determine the success of this role in an organisation.
  • Teamwork – although the data scientist role has received the attention of the industry, data science is actually a team sport, where a successful data scientist is typically supported by one or more data engineers. Teamwork – like in many areas of business – is critical for this role to see true success.
  • Problem solving – while a task at hand may guide and direct the analytical process of achieving the insights needed, the end goal should not stop a data scientist from exploring any interesting insights or unplanned paths/ challenges that may pop up along the journey. While analytically there may be a specific route that needs to be followed to gain the right insight, problem solving needs to extend beyond a siloed approach and must take into consideration other factors that could impact or result or a different outcome. A good data scientist is a problem solver and doesn’t get thrown off track by change.  ,

The hype around this role will likely not fade anytime soon. The skill is in demand but that does not mean that a qualified data scientist is necessarily hireable. In fact, as a guest lecturer in the analytics space, I have seen students experience challenges in finding work, despite the demand and qualification. Experience, along with these non-technical based traits, are almost always the reason behind this difficulty experienced. While experience has to be earned, holding these traits along with the qualification provides a good basis with which to sell the role effectively. Aspiring data scientists must onboard the need to develop such softer skills to master their job functionality – to not only land the job but to ensure relevancy of the data scientist role in the future.

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