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> this generalist thing is utter nonsense By your own description, you sound like a generalist with ability to dive deep into the areas needed. You seem to ref

> this generalist thing is utter nonsense By your own description, you sound like a generalist with ability to dive deep into the areas needed. You seem to refute the core premise of the article but your description says otherwise.

uptownfunk 8y ago
Honestly, I'm just put off by the word generalist. It makes it sound like just because someone can call a few APIs and create a deep learning model, that, as long as they have a clean data set, all of a sudden the world now revolves around them. Frankly, a lot of it is because of the hype, all of a sudden data scientists are these magical leprechauns that by virtue of their fancy algorithms can make money appear out of thin air. It really isn't like that, you really have to be able to go deep, as you said. Yes, there is a part of it where you have to be flexible (I wouldn't call it general, because I think it sweeps too much under the rug), so as to go so deep into a topic, you pass this kind of "expert-level turing test" where, were you and a domain expert put in the same room, a reasonable person wouldn't be able to tell you apart, where the weaker version is "another expert wouldn't be able to tell you apart" or something like that..
- wefarrell 8y ago
  It sounds like you equate 'generalist' with BSer, but the article's definition of generalist matches what you're advocating. > Specialists’ work is coordinated by a product manager, with hand-offs between the functions in a manner resembling the pin factory: “one person sources the data, another models it, a third implements it, a fourth measures it” and on and on. If you're taking the time to learn the domain, source the data and clean it then you fit their definition of a generalist.
scottlocklin 8y ago
The author of the HBR article works in a subject where literally nobody can be a specialist of the type he needs unless they work at his company. He's basically saying "this is the kind of data scientist I need; someone flexible enough to learn about my weird problem." He's also requesting a DS which can do everything from soup to nuts. That doesn't mean it's good for a DS to be a generalist. FWIIW I am a generalist DS type (arguably just generalist: database engines, blockchain, ML, DSLs, etc), but it's almost entirely because I'm easily bored. From an optimization of my paycheck and hours worked point of view, going deep in one area nets much larger paychecks and a nicer (aka fewer hours) lifestyle.

uptownfunk 8y ago

Honestly, I'm just put off by the word generalist. It makes it sound like just because someone can call a few APIs and create a deep learning model, that, as long as they have a clean data set, all of a sudden the world now revolves around them. Frankly, a lot of it is because of the hype, all of a sudden data scientists are these magical leprechauns that by virtue of their fancy algorithms can make money appear out of thin air. It really isn't like that, you really have to be able to go deep, as you said. Yes, there is a part of it where you have to be flexible (I wouldn't call it general, because I think it sweeps too much under the rug), so as to go so deep into a topic, you pass this kind of "expert-level turing test" where, were you and a domain expert put in the same room, a reasonable person wouldn't be able to tell you apart, where the weaker version is "another expert wouldn't be able to tell you apart" or something like that..

wefarrell 8y ago
It sounds like you equate 'generalist' with BSer, but the article's definition of generalist matches what you're advocating. > Specialists’ work is coordinated by a product manager, with hand-offs between the functions in a manner resembling the pin factory: “one person sources the data, another models it, a third implements it, a fourth measures it” and on and on. If you're taking the time to learn the domain, source the data and clean it then you fit their definition of a generalist.

scottlocklin 8y ago

The author of the HBR article works in a subject where literally nobody can be a specialist of the type he needs unless they work at his company. He's basically saying "this is the kind of data scientist I need; someone flexible enough to learn about my weird problem." He's also requesting a DS which can do everything from soup to nuts. That doesn't mean it's good for a DS to be a generalist. FWIIW I am a generalist DS type (arguably just generalist: database engines, blockchain, ML, DSLs, etc), but it's almost entirely because I'm easily bored. From an optimization of my paycheck and hours worked point of view, going deep in one area nets much larger paychecks and a nicer (aka fewer hours) lifestyle.