Global Big Data Conference

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.