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The Role of the Data Scientist

By   /  March 5, 2018  /  No Comments

Click to learn more about author N. R. Srinivasa Raghavan.

If there were ever a role that debunked today’s obsession with technology stealing our jobs, it is that of the Data Scientist.

The growth in Artificial Intelligence, Data Science, and Big Data Analytics has created a jobs boom, with Data Scientist being named the best job in America for 2018.

Yet for all this, the role of Data Scientist in the modern organization is far more talked about than understood. The term summons up images of a mysterious cabal of white coat-wearing people, beavering away on arcane topics of only theoretical interest to their employers. The reality is quite different – both in terms of the role and its practical importance to the organization.

It’s also why we should all be deeply concerned about the Data Scientist shortage, which could soon be costing billions in lost opportunities across a range of industries.

What Does a Data Scientist Do All Day?

Money may make the world go ‘round, but today it is data that greases the wheels. Each day, people and organizations create around 2.5 exabytes of information, in the form of structured and unstructured data. This information – thanks to its size, its format, and its dispersal among so many different platforms and silos – is a wasted asset without Data Scientists who can interrogate the raw data into insights that can be applied to solve real-world problems.

A good Data Scientist is more than a mathematician, statistician, or writer of algorithms – although these skills are obviously central to the role. Data Science is more than just number crunching: it is the application of various skills to solve particular problems in an industry.

This means you can’t drop a statistics graduate straight into a Data Science position and expect them to start delivering insight from day one. The job requires far more than theory – Data Scientists need to have a thorough understanding of the domains in which their insights will be applied. So, on top of maths, data engineering and visualization, a Data Scientist might also need a high-level knowledge of supply chain, finance, logistics, human resources, or any other line of business.

Little wonder, then, that Data Scientists can earn so much – and why they are in such short supply.

ROI of Data Science

Good Data Scientists do not come cheap. Recent research by Hired found that the average salary is $129,000 in the United States, with salaries significantly higher for more experienced experts.

But, used wisely, Data Science can represent one of the best bargains a business can make. By turning billions of bytes into actionable insight, Data Scientists can solve long-standing business problems, identify inefficient processes, develop new revenue streams or markets, improve data security, enhance customer service, develop tailored services – and provide answers to all the unknowns that a modern organisation faces.

Data Science might once have been a luxury, but that’s no longer true. Such is the business edge that the discipline confers on its users – such as speeding time-to-market, to take just one example – that organizations in practically every industry need Data Science to remain competitive. But getting their hands on the right talent is becoming increasingly difficult.

Skills in Short Supply

The scale of the Data Science skills shortfall is so large, you’d almost have to be a Data Scientist to make sense of it. McKinsey predicts that the US economy could be short as many as 250,000 Data Scientists by 2024.

To put that in perspective, around 8,000 people graduate with a Data Science or Analytics degree in the US each year. That’s great news if you’re one of the lucky few – less so for businesses that urgently need talent to unlock the power of data residing in your silos.

Even then, the low number of Data Science graduates is only part of the problem. As we’ve discussed, no one comes out of college with all the skills they need to make a difference in the corporate environment. No matter how many technical skills they have, graduates need several years at the coalface where they can learn to apply theory to the specific business challenges that they have been tasked to solve.

With this in mind, businesses must develop a strategy for how they acquire the talent they need, including offering the right incentives to attract the best Data Scientists and on-the-job training to ensure that they can start delivering value as quickly as possible. It might seem expensive, but businesses should see it as strategic, and as important any investment they will make over the next 10 years.

About the author

Dr. N. R. Srinivasa Raghavan has over 23 years of experience in the area of Advanced Analytics, optimization, Machine Learning and Data Science, which includes advising CXO’s on roadmaps for Data Science/Analytics/Data Governance implementation through green and brown field programs. He joined Infosys from Rolta where he was the head of Data Science and Big Data Analytics while leading a large team in creating software based solutions for the Advanced Analytics stack for Oil & Gas, Banking and Defense domains. He has worked in a similar capacity with leading Fortune 100 companies including General Motors, Citibank, and Reliance Industries. His seasoned experience in architecture and organizational model for Data Science, Big Data Analytics, and Data Governance helped him be a Strategic Advisor to CXO’s in Business and to IT heads. Prior to his industry stint, he was a tenured Associate Professor with the Indian Institute of Science where he established Decision Sciences Laboratory with funding from leading industrial houses such as Semiconductor Research Corporation (USA), Intel Asia, General Motors USA, SAP Labs India, Unilever, and Indian Government agencies like Department of Science and Technology, Dept of IT, Bharat Electronics, etc. Follow Dr. Raghavan and Infosys at: Twitter, LinkedIn

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