Top 5 Non-Technical Skills A Data Scientist Must Have



As IT professionals rush to upgrade their current skill-set and become career ready in the field of data science, most of them forget the other part of skill development — non-technical skills. In fact, our recent interviews with Tier 1 Leadership members from various analytics companies, reveal that non tech skills hold equal importance and complement tech skills.  

In this article, we we list down top 5 non tech skills that are essential for data scientists

1 . Communication Skills

Communication not just involves interaction with people, but also captures other areas as writing clearly, expressing tech ideas verbally aptly among peers/colleagues/superiors without apprehension. Data Science field is jargon-heavy and one should understand the concept well to explain it better to non-DS audience. 

Pro Tip: Engage in work conversations as much as possible. For a newbie in a DS job, this may seem intimidating at times but eventually you will pick up on your communication skills.

2. Teamwork

After communication, teamwork plays a pivotal role as individual’s tech skill is also tested on how well it gels in a team. Even though he/she is evaluated on individual merit, their ability to work with others is a telltale sign of tech skills aligning on the same page. As a result, more creative ideas spring up and stream towards improvement in work.

Pro Tip: A better way to building teamwork is healthy discussions held among team members. This way more interaction keeps coming towards work.

3. Problem Solving

A crucial skill that any company looks in potential candidates. Problem-solving also means how good and comfortable the candidate is in solving business problems. Since data science covers a bunch of problems,t data scientists have to demonstrate flexibility and creatively in solving issues.Although, this may seem hard at first for DS newbies at work, an intuitive mindset will go a long way in helping them along with experience.

Pro Tip: Thinking out of the box does not always happen. It needs time to acclimatize and master on a particular domain. As mentioned earlier, discussion with peers may give you the edge here and offer an interesting perspective.

4. Being Organised

One aspect which is usually never discussed explicitly is organising. It may be work-related or in general, the quality of being organised shows how good a candidate is at prioritising tasks, working on them systematically and resolving issues on time. This can also help them with time management so that work deadlines are adequately met.

Pro Tip: The customary way of note-taking/making lists will help anyone to start with organising. Also, try not to multitask many things at once. Focussing deeply on one task is better.

5. Business Awareness

Last but not the least is business awareness. This is a very critical skill that goes handy for every job. Not matter how proficient a data scientist is, it is redundant if he/she does not have strong business acumen. In fact, recruiting experts always look into this carefully before hiring candidates regardless of their technical expertise.

Pro Tip: The only advice here is to research thoroughly before making your mind about a particular job role. Every job role that involves data science will have a different requirement.

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Abhishek Sharma
I research and cover latest happenings in data science. My fervent interests are in latest technology and humor/comedy (an odd combination!). When I'm not busy reading on these subjects, you'll find me watching movies or playing badminton.

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