Data scientist vs. machine learning engineer careers Posted on : May 07 - 2020

There are many careers under the data science umbrella, including data scientist and machine learning engineer. But what's the difference between the two? Read on to find out.

Whether you're newly entering the workforce, have been recently laid off, are worried about keeping your current job or have been temporarily furloughed and have some time on your hands, there's no better time to pick up some AI-related skills than right now.

According to LinkedIn, artificial intelligence and machine learning jobs have grown 74% annually over the past four years. Job titles in this category include data scientists and machine learning engineers, but if you're confused about the differences between a data scientist vs. machine learning engineer, you're not the only one.

"To begin with, there was no distinction between the two roles," said Pragyansmita Nayak, chief data scientist at Hitachi Vantara Federal, which provides technology services to federal agencies.

When the two jobs first started growing, companies advertised for data scientists whether the job was more on the data scientist vs. machine learning engineer side.

"That confusion [still] exists today," Nayak said.

What is your background?

The biggest difference between a data scientist vs. machine learning engineer, experts said, is that they come from very different places.

"Data science has its foundations in statistics and in the business side," said Justin Richie, data science director at Nerdery, a digital services consultancy.

For example, a data scientist working at a bank might be asked to find out why customers are leaving, he said. The data scientist would decide on what data and analytics are needed and come up with a way to identify customers who are likely to leave.

Machine learning engineers, however, come from the other direction -- from software development.

"They're more focused on the production of the models and embedding them into applications," Richie said.

In the bank example, a machine learning engineer might take the model created by the data scientist and turn it into production code to embed into a mobile banking application. With that, the insights can become actionable, with the bank taking immediate steps to change the minds of customers looking to jump ship. View More