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software engineer or data science career

Data Science vs Software Engineering

Should You Consider Data Science As A Software Engineer?

In 2009, Google’s Chief Economist claimed “The sexy job in the next 10 years will be statisticians.” Nearly a decade later, his prediction is proving to be true—though we now call them data scientists. Are you debating between a career in data science or as software engineer? You’re not alone.

Hired’s 2018 State of Salaries Report found that data analytics roles pay similarly to software engineering roles, both bringing in an average of $137K in 2017 and tracking similarly in past years. In 2015, software engineering paid an average of $129K while data analytics paid $133K; In 2016, these numbers were $131K and $132K, respectively.

Related: Check out the Updated Salary Calculator in the 2021 State of Tech Salaries Report

So with similar (and sometimes higher) salaries, should software engineers consider careers as data scientists? As with most everything, it depends… but here we’ll review some of the factors to consider as you map your career path going forward.

Data science isn’t your thing? No problem

As the world becomes increasingly more data-driven, there’s no doubt that at least the basics of working with data will be important in any quantitative career.

But if you’re a software engineer who’d rather not spend the time and effort to beef up your data skills, rest assured that your career path can still be a solid one.

Pure software engineers have plenty of roles to fill outside of data science, from frontend development to infrastructure and devops roles.

And while data analytics certainly pays well, software engineering roles of all types are still in higher demand, according to our most recent analysis.

So if machine learning isn’t what you’re interested in, don’t stress too much about it. Instead, spend your spare time developing expertise in an area you are interested in.

Decide where you want to position yourself at a company

If you fancy yourself a lifetime independent contributor, choose a role that most closely aligns with your interests. Otherwise, consider your ideal role within the company and influence on business analysis and decision-making.

As a software engineer, you’re often closer to the product and use your skills to make them. This applies to consumer- or internally-facing improvements; better, faster, more user-friendly, etc.

As you progress in your career, higher levels of responsibility include managing a team of engineers. Some developers choose to go into more cross-functional product roles.

Data scientists, on the other hand, are typically more closely involved with the business side. They draw conclusions from data and produce business intelligence used to inform decision-making.

While software engineers are generally more focused on the technology, data scientists deal with statistics—and those statistics often come from user data collected from the product that’s been built by the software team.

Given their proximity to important business metrics, data scientists can expect to interface more with senior stakeholders on non-technical teams.

When comparing these two roles, consider the skill set needed for each, and where you see yourself in the business and which types of stakeholders you prefer.

If you like building products and interacting with other technical people, software may be a better bet. Those who thrive on analyzing complex datasets and communicating insights to less technical colleagues may prefer data science.

Machine learning changes the game

As machine learning becomes an integral part of many new products, there’s more overlap between software engineering and data science. This blurs the lines while making it easier for technical people to choose between the two.

Regardless if you become a full-time data scientist, it’s a good idea for any software engineer interested in products involving machine learning to understand the basics. Examples of these products use image recognition, bots, or natural language processing.

At the end of the day, choose a career path based on your interests and strengths. In this case, average salaries for data science and software engineering are similar. Before committing yourself to one or the other, experiment with different types of projects. Interact with different parts of the business to see where your personality and skills best fit.

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Related resource: Hired’s 2021 State of Software Engineers Report

Blog revised Dec. 22, 2021