Comment by reureu | Hacker News Reader

Comment by reureu | Hacker News Reader

This is an article making baseless generalizations that all data science is failing in Silicon Valley, and that the true need of a company is to hire a "data strategist." Unsurprisingly, the article is written by a self-proclaimed data strategist.

This blog post would be more compelling if there were citations or even general context backing up the authors pretty wild claims ("the vast majority (we are talking 80-90%) want to leave their current job", "failure rate of data science teams is over 90% right now", "the top 5 data scientists I have ever worked with, only 1 had a PhD").

As a data scientist working for a relatively well known startup in the Bay Area, I don't dispute that there are problems in this industry. There are many under-/un-qualified managers running around (I don't know if that's a data science specific problem, or if it also applies to much of engineering as well). There are ICs and teams that have trouble producing industry-applicable work because they're too academically focused. There are also business-driven teams that have trouble producing quality/reproducible work because they're too business focused.

That said, the claim of 90% failure is ridiculous on its face. Having another MBA put together a powerpoint presentation about a company's data strategy isn't going to suddenly solve the actual issues in data science.

That was also my impression. It's kind of ironic that a Data Science/Strategist writing would be so heavily opinionated and have so little in term of data.

Maybe that's because the strategy is to produce bullshit

Ha, I like that the grammar of this distinguishes between "there is no strategy" and "the strategy is, actively, to produce bullshit" and lands on the second option, which jives with my experience :)

Felt the exact same way after reading this, thank you. The quotes on failure rate and 'vast majority' without any data to back it up, coming from someone who is a data strategist, was a little odd.

In my opinion, I feel like we are at peak "!@#@ data science! @#$@", and there will naturally be some deflation and correction in expectations just like any other occupation that comes out of the woodwork and is the 'sexiest job of the _____'. It's just the way it is. The article author seems to be carving himself out a niche to capitalize on this (which is fine).

> in Silicon Valley

Ironically, I assumed he was talking about NYC. Apparently he's actually in Minneapolis

> There are many under-/un-qualified managers running around (I don't know if that's a data science specific problem, or if it also applies to much of engineering as well).

This is a problem no matter what line of work you're in.

> This is why the failure rate of data science teams is over 90% right now.

It is unsupported in the article, but on its face, isn't a 90% failure rate about right for bleeding edge tech efforts?

This was pretty much my reaction.

I considered flagging the article but decided against it after reading your comment, which I hope remains visible.

What do you mean by "too academically focused" ? Developing new methods?

Yeah. Academia doesn't care about interesting applications, they care about novel methods.

"I made a huge difference for the business by being thoughtful about feature selection and then applying a bog-standard regression method" isn't appealing to the academic mindset.

To be fair, people are chasing a force multiplier here. Yeah, that is a buzzy term. :( No I don't like that.

So, what I mean is that people are looking for something where they can apply one team's work and use it across many teams. The more the better. The idea is that you don't have to have many people being thoughtful producing methods, you just need many working hard applying them. If there is a better term for that, I'm game to use it.

I think an academic would be scientific in a data science role. You don't have to invent a new algorithm or tool.. just take the right approach.

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