1. Sexiest job of the 21st century: Here’s why ‘data scientist’ is the coolest one

Sexiest job of the 21st century: Here’s why ‘data scientist’ is the coolest one

Despite the fact that a data scientist’s job has been listed as one of the hottest jobs, a popular notion doing the rounds is they will not be required once automated programs perform similar functions. Nothing could be farther from the truth

Updated: October 3, 2016 7:05 AM
It needs intensive training to polish technical and non-technical skills, such as knowledge of analytical tools and computer science, intellectual curiosity, communication skills, and business acumen. (Reuters) It needs intensive training to polish technical and non-technical skills, such as knowledge of analytical tools and computer science, intellectual curiosity, communication skills, and business acumen. (Reuters)

Data scientists, the “unicorns” of data, are professionals with a unique blend of knowledge and skills in data mining, machine learning and analysing statistics.

A data scientist’s role is to study the enormous amount of raw data, and make it accessible and more valuable for an organisation. It needs intensive training to polish technical and non-technical skills, such as knowledge of analytical tools and computer science, intellectual curiosity, communication skills, and business acumen.

In 2012, Harvard Business Review named data scientist the “sexiest job of the 21st century.” Glassdoor has called it the “best job of the year” for 2016.

Now, despite the fact that a data scientist’s job has been listed as one of the hottest jobs, a popular notion doing the rounds is that they will not be required once automated programs perform similar functions.

Nothing could be farther from the truth. We need to understand the criticality of data scientists’ role in today’s complex business world objectively.

As data science and its role in an organisation evolves, the function of data scientists and their role will continue to develop. Data science is viewed by many as building models. However, building models is just one of many steps required to deliver the impact that data science can.

To solve a business problem, it has to be converted into a data science problem. One needs to put together an analytical plan. Next is identifying and locating the data that will be required to run the analytics, and finally integrating the results of the model with business systems. And then there are softer issues that are needed to enable data science to be successful in an organisation.

A data scientist is required for all these steps. Now, while a lot of automation is happening in the field of modelling, we will still need data scientists to make data science happen.

Data science is becoming part of almost anything we do, consume or produce. Who would have thought that many of the journalists will have to become data scientists to be successful in their profession. Take the example of the Panama Papers case—the 11.5 million leaked documents that detail financial and attorney-client information for thousands of entities. Even a joint team of journalists from across the world will take years to comb through the papers. Data science is making the process quicker—helping journalists understand the information contained in the documents, map relationships between various entities and so on.

Another example is the automotive industry, which is primarily about designing and engineering vehicles that people want to buy. Now, with the development of autonomous cars, data science will play a big role in determining how well a car will drive. It won’t be surprising if, in the near future, the success of a car company depend more on the quality of its data science team rather than its design or engineering team.

We are seeing similar developments in other areas as well. All this means that data scientists, instead of becoming extinct, will drive all professions in the world.

Anil Kaul

The author is CEO & co-founder, Absolutdata Analytics

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