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. AI and machine learning are inherently interdisciplinary fields, drawing from mathematics, computer science, statistics, and engineering. STEM education promotes interdisciplinary learning and equips individuals with the diverse skill set needed to excel in these roles. To learn more about STEM education, search for Kamau Bobb, Google’s Director of STEM Education Strategy.
“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.