Is Being A Data Scientist Really The Best Job In America?

Is Being A Data Scientist Really The Best Job In America?

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Is Being A Data Scientist Really The Best Job In America?

Glassdoor’s service allows employees to anonymously rate their jobs and their employers, awarding scores for how well they are paid, treated, and helped to advance in their careers.

Data Scientist came out with the overall best score in the 2016 report, and this is great for several reasons.

The big data and analytics revolution is only just getting started, and companies are crying out for talent that can help them unlock the insights hidden in their data. Recognition of the benefits will encourage more people to consider a career in a field where new blood is desperately needed.

To many, it might seem surprising to pick data scientist as the “best job”. Sure, it is well paid and you get to tell people that your job is predicting the future – but is it really better than being an astronaut, professional athlete, or lead singer in a rock band?

Well, as none of those jobs appear anywhere in Glassdoor’s ratings, I think we can assume that there wasn’t enough data on those professions to make a worthwhile evaluation. But while the job of data scientist may on the face of it seem to be lacking in glamor compared to some, the idea that you will be confined to an office crunching numbers for your entire career is outdated. The day to day life of a data scientist can be remarkably varied as several people well established in the field were keen to tell me when I spoke to them about the Glassdoor ranking.

As Greg Gordon, vice president of the Big Data practice group at analytics-based workforce solutions provider Kronos tells me, “It’s not sitting in a room all day – we take our work and apply it to customer problems. We’re working and interacting with customers on a daily basis talking about real problems, then attempting to replicate, model and solve them.”

One of his team members, Alex Krowitz, a 20-year veteran of data science, certainly agrees – “It’s rewarding because you get to see the customer’s eyes when they realize you can provide a comprehensive analysis of their whole business.”

An increased focus on working with customers to understand their needs means businesses are casting their nets wider in terms of talent they are seeking.

Tye Rattenbury, director of Data Science at Trifacta, says “If you look at a data science job description from five years ago, it was basically ‘advanced degree, computer skills, predictive modelling’.

“Now that’s only a third of it – the other two thirds are ‘works well with others’, ‘knows how to report and communicate’. It’s great when people are smart and can do clever stuff, but they need to be able to feed it back into the business so we can do something about it.”

That diversification of skillsets is partly due to the way the role, and demand for the skillset, has seeped into the structure of organizations. Whereas once Data Science would have been an isolated pool of talent, now it is starting to permeate individual departments.

As Rattenbury explains, “The modern version is about taking that centralized data science team and flat-out splintering them – saying two of these data scientists are going to marketing, one to product design, one to sales … and they’re going to be fully embedded in those teams.”

The appeal of solving real-life problems with practical solutions, is clearly a big part of the job’s attraction, too. Modern data science at its most forward-thinking is about using very fast, very large incoming datasets to solve problems in real-time, as they occur, meaning that results are often instantaneously visible.

I also spoke to Mark Schwarz, VP of data science at Square Root, who told me “Back in 2003 I wanted to work in data science so I could stand in an elevator next to a sales or operations VP and be able to succinctly explain to them what I did every day. I was a technical expert but virtually all of my time was spent collecting data. We all assumed that someone, somewhere was going to then make good use of that data to drive the business forward in thoughtful ways. In most cases, actually no one was.

“I moved to more and more data-focused roles to actually put that data collection to use. I wanted to be able to stand next to a VP and say ‘here’s how my team grew revenue or profits’. Now I get to do that.”

So would he agree it’s the best job in the world? “I love this work, but it’s not for everyone. It’s messy. It is absolutely true that many data science organizations spend 80% of their time cleaning and preparing data and 20% moving it to be actionable.”

Personally, this is another aspect of the job which I expect to see change in the future, when more and more of the routine work involving data cleaning, compliance and accountability is likely to become increasingly automated. But for now, it’s part of the job.

“For me, that first 80% of prep time is adventurous, creative, valuable and loosely structured enough to be full of growth experiences,” says Schwarz. “For others, it’s not as glamorous as they might expect.”

But is it all just about the money, really? In a hypothetical parallel universe, where data scientists were paid minimum wage and fast food restaurant workers earned six figure salaries, would you change career?

“Well, I want to provide well for my family, so I would probably consider it”, Schwarz muses. “But I know I would miss the succession of learning curves that are part of data science.”

Bernard Marr is a best-selling author, keynote speaker and data expert. His new books is: ‘Key Business Analytics: The 60+ Business Analysis Tools Every Manager Needs To Know

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