Why data science isn’t primarily for data scientists anymore

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Data science is quite the hot career field today. It’s got it all: big salaries, a cool title, and a cutting edge mystique. You can’t turn a figurative corner in the business world today without finding data scientists, or people who want to be a data scientist. 

After all, why should you earn £75-100k per annum as an analyst or a statistician, when you could be earning £130-165k as a data scientist? 

But businesses need to cool down the love affair with data scientists, without in any way getting divorced from data science. Data science has evolved in the last few years, and it’s not just for data scientists anymore. 

Doesn’t take a scientist

The move to cloud data science has changed data science jobs, but the assumptions about data science jobs haven’t kept up. New cloud-based, self-service business intelligence (BI) platforms like SiSense make it easy for users to run their own ad hoc analyses now, circumventing the need to code queries. 

Platforms that automate model building, provide pre-built libraries, support and simplify analysis, and perform other tasks that used to be carried out by data scientists have broadened and democratized access to data science.

“There is still much work to be done, but AI can both remove much of the tedium of trying to figure out how to connect all of this data as well as to help data professionals understand the data to determine how it should best be leveraged. This includes such things as AI-powered data cleansing and modeling as well as general statistical analytics of the underlying data itself.” says Inna Tokarev Sela, who leads the AI team at Sisense, a business intelligence software company that recently rolled out natural language query (NLQ) capabilities. 

Indeed, with AI-powered data tools, you don’t need a proper data scientist.

What’s more, between the new data manipulation and analysis tools and the increasing importance of data to all areas of business, everyone is managing data to some extent or other today. Work that once was thought of as a task for data science is now part of the daily workflow for analysts, managers, developers, and other employees throughout your business.

“Because of the prestige of the data scientist job title, companies like Lyft will hire for data science job titles, but with data analyst skillsets, resulting in an even more skewed picture of what constitutes a ‘data science’ job,” notes data consultant Vicki Boykis. This isn’t a good thing.

What happens when you have too many data scientists

When you hire a data scientist unnecessarily, you end up paying a high salary just because of the title. Then you discover that you’ve hired someone who doesn’t have the right qualifications for the work in front of them, and lose out on the skilled statisticians, analysts, and engineers that you really need to keep the business running, all because you were seduced by the siren call of data science.

Jonny Brooks-Bartlett, data scientist at Deliveroo, puts his finger on the crux of the problem. “Now if a data scientist spends their time only learning how to write and execute machine learning algorithms, then they can only be a small (albeit necessary) part of a team that leads to the success of a project that produces a valuable product,” he says. 

“This means that data science teams that work in isolation will struggle to provide value! Despite this, many companies still have data science teams that come up with their own projects and write code to try and solve a problem.”

Without the engineers, analysts, and other team members that you need to complete your projects, you give your data scientists work that they are overqualified for and don’t enjoy. It’s not surprising that they then deliver poor results. Unfortunately, they also get in the way of the work that needs to be done by engineers or developers. 

Iskander, principal data scientist at DataPastry, puts it succinctly. “I have a confession to make. I hardly ever do data science,” he admits That’s because he’s repeatedly asked to fill roles that don’t require his specialized skills.

Is this the end for data science?

No, we’re not about to push all our data scientists off a cliff. The business world still needs data scientists. They have a valuable role to play in ensuring that your science is clean and reliable, and that your app can work the ways that you think it should. We’re not walking away from our relationship with data science; we’ve just reached the end of the honeymoon. 

The truth is, letting go of the data science fantasy is best for all of us. It will be a win-win-win situation, because data scientists can go back to doing what they are really good at and enjoy, instead of having to fill engineer or developer roles; businesses will have the employees they need in the right positions, without paying inflated salaries; and analysts, statisticians, engineers, and the like can have back the jobs that they love, without having to pretend to be data scientists. 

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