[gipp]

> Well, it certainly isn't helped by data scientists claiming to be better than ANY programmer and ANY statistician.

I don't really think that's much of a thing? I've been working in the field for several years now, and I'd say majority of my efforts when communicating with stakeholders is about _qualifying_ our capabilities and managing the expectations they're already coming in with.

[gaius]

It's quite a famous quote, I think it was the chief data scientist at LinkedIn who coined it originally.

There is real value in being a "statistical programmer" but that value can't presently be seen past the smoke and mirrors.

[gipp]

I think you're thinking of Josh Wills, Cloudera at the time, now Slack:

https://twitter.com/josh_wills/status/198093512149958656?lan...

> Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.

A lot less braggadocious than what you're suggesting, it's just talking about it as a jack-of-all-trades type of job.

[gaius]

"Average" would have been more realistic, and as I say there is value in the role. If DS keeps promising and failing to deliver miracles, it will never be more than a fad. Someone with a job title of "applied mathematician" already does was DS claims to do... the title of "statistical programmer" or "statistical engineer" is a better one, than "data scientist".

[gipp]

Yeah, I agree completely, I just don't think that the hype of the role is coming from practitioners.

I know this isn't really your point, but I've met Josh Wills, for example, and listened to many of his talks. I don't think I've met a more realistic guy (among actual practitioners) when it comes to the expectations and reality of doing corporate data science. The hype, I'd say, is just an emergent phenomenon of the tech reporting cycle. Nobody's out there _trying_ to inflate expectations, except a few consultants and "thought leaders" maybe.