Should you hire a data scientist for your development team?

The future is data

Almost everyone in the development world knows how useful big data can be internally and with clients. So does that mean you should hire a data scientist?

It’s no secret that data is crucial to compete. In fact, 70 percent of Fortune 1000 companies specifically called out big data as critical or very important to their success. Given this move towards data, development teams around the world have begun asking themselves whether or not to hire a data scientist.

IBM predicts the demand for skilled data scientists will increase 28 percent by 2020. A McKinsey study found that about half of executives noted they had more trouble finding analytical talent than any other kind of role.

At this point, most of the bigger companies have armed themselves with a data science team. While there are difficulties scaling a data team due to the new nature of the type of work, those who can afford it are trying to collect as many skilled data scientists as possible.

But what about small- and medium-size businesses that can’t necessarily afford to hire a full-scale data team? Is it essential to add a data scientist to the development team? With all of the information about the data skills gap, it seems like a smart move to hire a data scientist. However, the real answer lies in how much data you’ve got and what type of work your development team does.

Why should you hire a data scientist?

Most companies seek out a data scientist to help improve their bottom line. That’s because data scientists can provide better customer targeting and use predictive analytics to forecast consumer behavior.

Some companies claim their data teams can predict the mood of their audience and change ads accordingly.


These skills, of course, are hugely in demand because they give the organization a competitive edge.

Data scientists, when given the proper tools, can also save the company money by calculating spending and where to cut costs. They can identify low performers (from personnel to products) and help the business side understand how to save resources.

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Given these benefits, it’s no wonder that this job is in such high demand. But, again, does every company need to hire a data scientist? Let’s take a look at specific technologies to move closer to the answer.

Hiring based on technology

One way to decide whether or not to hire a data scientist involves examining the technology your company uses now and what it plans to use in the near future.

Artificial intelligence

Executives are investing heavily in the booming AI market, and developers are at the forefront of AI teams. But AI relies on data to learn, which means a data person is likely necessary to have on the team. There is talk that AI will replace developers; however, what’s more likely is that data scientists will join the AI team to improve the algorithms, allowing the technology to improve itself.

Related: Data bias in AI: Should a small business care?

Predictive analytics

Developers are beginning to use predictive analytics in the testing process. By analyzing big data sets (which includes historical and trend data), developers can better understand what to test and when.

While bringing a data scientist into the testing phase can result in fewer bugs and rework, it’s not always necessary, depending on the company. Organizations that continually release bug-filled software should consider hiring a data scientist to improve the process. However, companies who have a QA engineer might not need a data scientist.

The Internet of Things

IoT produces an incomprehensible amount of data, and companies that specialize in IoT will likely be on the hunt for data scientists (if they aren’t already). When CIO named the top 10 most in-demand skills for IoT, it included big data and machine learning — two areas that data scientists specialize in.

Hiring based on volume

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Another way to evaluate whether or not to bring a data scientist onboard is to consider how much data there is internally (or from clients). Companies that don’t collect much data don’t need a data specialist. Some of these low-data companies instead opt to hire a data analyst or someone who can work with internal teams (such as HR, sales and marketing) to improve processes and forecasting.

Businesses that do collect a lot of data (or companies whose product is data) should already be looking for a data scientist, a data engineer or a business intelligence developer (BI developer). The latter can help explain the data to the business side, so they can make data-driven business decisions.

Related: 5 best practices for customer data management

The bottom line on data scientists

There is a lot of talk about automating data scientist jobs. But as of now, that’s a pipedream. Plus, there will always be a need for someone to create the algorithms that automate certain data tasks. Translation: If your company works with big data, you should probably try to hire a data scientist rather than wait for automation.

As you start to put together your data team, there are a lot of factors to consider. Some companies that rely heavily on customer understanding and prediction might benefit from building an entire in-house department, while most companies only need to hire a single data expert or to outsource the task for particular projects.

What is becoming clear though is that as we advance into the future, these roles will become indispensable to every company, regardless of their industry.