By Dhrumil DhakanThe applications of Artificial Intelligence are growing every day and are penetrating into every section of society. And the industries now have to chart careers according to the technology. Let’s take a look at the insurance sector. The need for an insurance agent is minimizing by the day as the whole process has been digitized by the companies to a point where the customer can buy car insurance or other insurances from the comfort of their home.
It is said that the pandemic has fastened the Applied Intelligence process manifolds and has also led to the need for a higher number of data scientists that bring value to the company. But what are the ideal attributes and skills that are needed, especially in the next five years with the ever-changing markets?
Speaking at the recently held ‘Your Future in Data Science’ panel at the Applied Intelligence Week by Accenture, Dr. Rajamani Sambasivam, Chief Data Scientist, Petronas Group said, “An ideal candidate should know the algorithms and be familiar with the mathematics. He should know the technical stuff and in addition to that, a data scientist is none other than a problem solver.”
With the swift adoption of AI tech by almost every industry, it comes to the data scientist to be agile with their solutions for the problems that they have not faced before, as no problem is similar. Working on logic and sharpening the out-of-the-box thinking skills is also that can be quite useful.
“One of the things that are going to be important in the next five years is that the market will offer a lot of ease-of-use technologies but this doesn’t mean that we don’t need to understand the mathematics or the basic principles. So there is a real danger in using all of these easy-to-use tools is not understanding the principles of the tools,” added Fernando Lucini, Chief Data Scientist, Global Machine Learning Engineering Lead, Accenture.
The fast automation process has simplified the processes, but it is upon the data scientist to understand the inner workings of the tools and have a balance to not just perform the maths but to truly understand it and finally, be able to apply it in an innovative way.
“I personally feel that a person must be good in his domain knowledge, he must retain and continually improve on it. I also feel that coming from a management background, a person must be able to articulate a business problem into a data science problem,” Prof. Chandrasekharan Rajendran, Professor, Department of Management Studies, IIT Madras, gave his perspective on the subject.
The changing trends of the market demand a data scientist to be familiar with the fundamentals of the domain they serve and this knowledge will be indispensable to them throughout their career.
He also advised the data scientists to keep testing themselves by participating in international competitions such as Kaggle that would help them understand where they stand when it comes to knowledge in terms of the international standards.
“Being good in AI, engineering, ML, and developing statistical problems is one thing and being good at complex business problems is another thing but merging these two, I figure, is the real way we need to bring this all together. So, the ideal data scientist candidate for me is able to translate real-world problems into data science problems and be able to solve them,” states Tahney Keith, Managing Director, Applied Intelligence at Accenture Australia
Not just that, the data scientist should be able to apply the outputs of the data science problem into the business by communicating them through the way of insights or actions, that would help even the non-data scientist understand the issue.
