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Top 5 Soft Skills All Successful Data Scientists Possess


Top 5 Soft Skills All Successful Data Scientists Possess

data scientist


data scientist

Data science is one of the hottest fields in the industry and whenever there are job openings, throngs of data science experts as well as enthusiasts flock for them. In our many interactions with numerous CXO-level leaders in this sector, one of the key things pointed out was that while there is no dearth of data scientists in India, companies, especially bigger, established organisations always struggle to find the right talent.



But why is that?

Industry experts say that simply hiring a data scientist is not enough. Managers need to take special care to align business and data teams thus enabling data scientists to be self-sufficient. Otherwise, they might not get the expected ROI in data science which is a problem almost 80% of the companies face.

In this article, Analytics India Magazine will put light on the top soft skills most successful data scientists possess:

Communication

Communication is the backbone of almost every job profile. This is especially true with data scientists. Conveying exact information or a problem statement — be it in the form of algorithms of interaction with peers — is crucial to success. It showcases a clear thought process and an uncluttered outlook on the part of the data scientist as well.

Problem-Solving

While it is important for a data scientist to keep themselves abreast on the latest tools and developments, it is mandatory for them to work on solving problems. A data scientist is like a doctor, the more problems they solve and more experience they have, they get better in their job. That is why companies value experience a lot more than the educational qualification. But it is important to have the basic educational qualification. A full-time course will be valued more than an executive course.

Ability To Draw Parallels To Real-world Problems

If a student wants to choose Data Science as a career, they should start paying attention to subjects such as Statistics, Probability, Algebra, Set theory and Data Structures and Algorithms. If they are strong with the basic concepts, then they can use the technology tools to their advantage to build great models.

While a lot of theoretical knowledge can be gained by doing these courses, their learning would not be complete until it is applied to practical problems. Industry mentors can play a vital role in this aspect. They will also help in understanding the practical difficulties in applying their knowledge to real-world problems. This will also help them in building their domain knowledge that will help them to be a good data scientist.

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Prioritisation 

A good data scientist always has his/her priorities straight. Because as a data scientist, he/she is bombarded with many questions, which can be answered in numerous ways. ”First, you need to decide which of those questions are actually worth answering and how much effort is worth putting into those questions. If someone asks you for a detailed description of how your users <x> with lots of demographic breakdowns and trends, maybe the first thing to do is figure out how many people actually <x>. When you find out its 0.01% of your population, which you can probably know in ten minutes or less, you should realise <x> isn’t worth understanding and convince your requester to find another question (persuasion coming into play again),” writes Chris Luhrs, a data scientist.

Business Acumen

Data scientists deal with such enormous information that if left “untranslated”, becomes meaningless for the upper-level management. Sahana Shetty, the HR Leader for Technology at ANZ, told AIM in an interview, “We also look at a data scientist’s business acumen while hiring him/her. We are keen to have individuals who are high on potential, who exhibit a growth mindset and have an inclination to learn and grow.”

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