A recent panel at a DataScience SG meetup, hosted at Google, took on a question that has puzzled many in the industry: what exactly separates a data scientist, a data engineer, and an ML engineer? The session brought together practitioners to share their perspectives on how these roles differ in practice, rather than just on paper.
The panel's core value lies in its attempt to demystify roles that are often used interchangeably in job postings and team structures. By clarifying the distinct focus of each position, the discussion offers guidance for organizations looking to build balanced data teams, as well as for individuals deciding which career path to pursue.
While the source provides only a summary of the talk, its premise is clear: understanding these differences is essential for effective collaboration and hiring. For anyone navigating the data field, this panel serves as a useful starting point for mapping out where their skills and interests might fit best.