​Following the last module whereby I shared the technical skills needed to become a data scientist, I am often asked another question, “How can I be a GREAT data scientist once I have gained the technical know-how?" 

Firstly, it is impossible for anyone to pick up all the technical skills to be an outstanding data scientist because the field is constantly evolving. Secondly, one needs to build on his or her soft skills, besides being technically competent, to excel in this field.

So what are the fundamental soft skills that data scientists should possess? 

Research 

A data scientist is similar to a solution provider who needs to find nuggets of information and insights to help organizations overcome their current challenges or to move them along the path of continuous improvement.

As a data scientist may not know everything about their projects, there is a strong need to research extensively. It could be researching for the techniques to tackle class imbalance, integrating different technologies or new machine learning techniques, or the very simplest and common task of looking for suitable functions in R or Python.

Having good research skills (i.e. finding the right information quickly) helps cut down time which the data scientist can use for more important tasks such as data exploration, data management, feature engineering and improving code readability.

In order to find the right information quickly, the data scientist has to use the correct keywords in the search engines to find the desired results. With search engines' auto-complete function in place these days, it is possible to get the required results very quickly.  



Willingness to learn

Given the many fields and areas that a data scientist are involved in, being able to learn efficiently is important. The more a data scientist learn or pick up new skills and knowledge, the more value he/she can provide to the organization.

Being able to pick up knowledge quickly is perhaps one important aspect of efficient learning. The other is being able to relate to different facets of business or concepts, and making the right connections with a vast array of knowledge. Being able to make the relevant connections help to retain not only the knowledge, but also strengthen understanding.

Each of us has our way of learning so it is important to find the best way to learn and apply it, especially in the data science field.

On the other hand, the working environment of a data scientist is very dynamic with numerous, challenging changes. Sometimes, it is necessary for a data scientist to adapt quickly and “un-learn” something in order to learn new things. In short, a data scientist has to constantly question their deep-seated assumptions and concepts as well as their relevance.


Teamwork

Being part of a team is necessary for anyone who works in data science. They have to learn how to be a good leader as well as a good follower, especially since a data scientist has to communicate regularly with both data engineers and business users. 

Everyone has a part to play in making the team effective and efficient. The leader cannot be effective without the cooperation from the team members while team members cannot be effective if the leader does not stay focus, give direction, manage timelines and motivate the team members.

Communication 

To be a great data scientist, communication skills are very important. Being able to communicate the relevant insights and in a manner that is digestible by management is imperative for a data scientist. For instance, presenting insights that are clear, concise and structured so that they can be easily understood.

A good data scientist would also need to learn how people learn and establish the most effective communication channels in bringing across the messages and insights clearly for better decision-making.

Empathy

To be a good leader, follower and communicator, a common “ingredient” is having empathy which is putting ourselves in other people's shoes and thinking from their perspectives.

Having empathy allows one to understand which behavior are likely to be chosen, which perception is likely to be taken if the "story" is presented in a certain way. This allows the data scientist to anticipate the possible outcomes and also come up with the appropriate presentation.

For instance, if a data scientist has to present on an an ad-hoc basis, he/she should be able to anticipate the questions that the audience is likely to ask, and prepare the answers and data in advance. Being able to anticipate and answer these questions will enhance the credibility of the data scientist.

Evolving complementary skills 

Whether you are planning to become the next great data scientist or are in the process of hiring one for your organization, this individual will have to be able to balance the hard skills with soft skills. And the combination of these two can only be achieved through experience and practice to ensure personal and business success. 



Written by
Koo Ping Shung
President and Co-Founder, AI Professionals Association
Co-Founder and Practicum Director, Data Science Rex Pte. Ltd.
Industry Innovation Mentor, AI Singapore