Re:Model Explainability is The Next Data Science Superpower
by disgruntledphd2 on Monday March 11, 2019 @08:54AM
(#19358567)
Statistics is boring and uncool.
ML is hip and profitable.
Hoenstly, it's mostly garbage. The original notion of data scientists was invented by FB for a very specific set of skills (social science PhD's with Map-Reduce and experimental design), but it's a cool title and thus it got spread across multiple roles.
It's super weird though, despite doing data sciencey work for about a decade now, when I changed my title on LinkedIn to be data scientist, I started getting offers for jobs that paid a lot more money, so there's an incentive on the candidate side to re-brand.
But yeah, ultimately all the job is is some stats, some code, and some communication. Don't get me wrong, its a great job and its hard to find people who are good at all of this, but in my experience the limiting factor is definitely the statistics and the domain knowledge rather than the code.
ML is hip and profitable.
Hoenstly, it's mostly garbage. The original notion of data scientists was invented by FB for a very specific set of skills (social science PhD's with Map-Reduce and experimental design), but it's a cool title and thus it got spread across multiple roles.
It's super weird though, despite doing data sciencey work for about a decade now, when I changed my title on LinkedIn to be data scientist, I started getting offers for jobs that paid a lot more money, so there's an incentive on the candidate side to re-brand.
But yeah, ultimately all the job is is some stats, some code, and some communication. Don't get me wrong, its a great job and its hard to find people who are good at all of this, but in my experience the limiting factor is definitely the statistics and the domain knowledge rather than the code.