Data science title confusion
Job titles for data science related roles are just too confusing, especially in the Bay. Different companies have different definition for data scientist, data analysts, research scientist and machine learning engineers. In my opinion, the definition should be as follows: Data/business analysts: Analyze data to support business needs. They do ad hoc analysis and build dashboard Data scientists: Focus on more challenging and longer-term projects, using machine learning or statistical methods to solve problems, either for analysis use case or prototyping algorithms behind product Research scientists: Research on cutting edge technologies and publish papers Machine learning engineers: Build out scaled solutions and add new ML powered features to the product. A lot of coding involved. However there are lots of companies calling sql monkey as data scientist analytics, and calling applied ML as research. I understand it’s for receuiting purpose, but it makes no sense to me. Do you agree those roles should be called what they actually are?
Read somewhere that Lyft changed to DS titles because they were losing analyst candidates who wanted to be called DS. Might've been after the trend already started, but that's the case none the less. Costs nothing for a company to give a title, but going through another recruiting cycle is $$$. Not really sure what to legitimately call full on data prep "SQL monkies" like you say either. They're not really analysts in many cases. Data engineer is closest, but that kind of devalues the more in depth skill sets involved with that title. The whole market is a mess with job titles.
Data engineers work more on building data pipelines, I’m not referring to that. By sql monkies I mean just doing ad hoc analysis every day using sql. For example, FB DS Analytics and Lyft DS fall into this.
Same happened to FB many years ago, now the actual Data Scientists are called something else to run away from analytics type of work. LOl
So I’ve seen you post before and I assume you’re trying to pick on Facebook with your post, but I want to say, that at FB, product analytics DS do exactly your “data scientist” description, and research or core data scientists do your “research” description. Some research scientists do the ML thing it depends on the area. Some data scientists might do your data analyst description but they are either extremely junior, or really bad and en route to getting let go. FWIW I agree with your post :)
When you devalue people’s work by calling them “sql monkeys”, of course they won’t want the title analyst and will fight for a title where people will respect them. Even if it’s not the work you want to do, sql-based analytics is important and drives huge business value, and somebody’s gotta do it. Maybe if other data professionals respected their work just a little, they wouldn’t feel the need to fight for a different title. I do totally agree with the way you’ve laid out the titles though. That seems correct to me as a mapping of what responsibilities should go with each title.
Make sense, it’s all about respect for work.
The ratio of analytics focused people versus research/ml focused people needed at majority of companies is about 80/20 FB was smart enough to realize this awhile ago. Others are catching on now. Your PhD has a use case and all.. it’s just not that many of you guys are actually needed.
Who cares, as long as it gets relatively standardized