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It's mostly work ethic I find. I've met quite a lot of Black Swans, and been employed alongside precisely zero. I know one hard science PhD who runs their own

by urthor 5y ago

It's mostly work ethic I find. I've met quite a lot of Black Swans, and been employed alongside precisely zero. I know one hard science PhD who runs their own K8s cluster at home and plays with Linux distros. They describe themselves as "a statistician who can program." Generally speaking it's more common for them to come from the math side of the fence. From the IT side I'll say the math is a bit harder than the computer stuff. It's genuinely, 100% work ethic.

musingsole 5y ago
> I know one hard science PhD who runs their own K8s cluster at home and plays with Linux distros. That's super awesome for that data scientist, but the question for a business is can/should you structure yourself in such a way that you NEED employees with that cornercase level of joint expertise. The answer is you really can't. Individuals have awesome strengths that they developed for reasons particular to them. Use those strengths when you can. But the business has to rely on a common denominator of a role or else it'll never fill it when their unicorn leaves to go backpacking in Europe.
- urthor 5y ago
  Agree. You need to structure your talent pipeline, and organization, based on the average level of talent you can likely receive at your compensation bracket. You cannot create a single point of dependency on an employee who you'll never be able to replace for the same amount of money. However, the issue is that productivity is logarithmic. The unfortunate truth the school of hard knocks has shown me is that someone without the "roll your sleeves up" attitude to learn Docker is generally speaking just not going to be that effective when push comes to shove. Now if you're using tools to abstract the time of data scientists who are CAPABLE of learning Docker, that is a different story. But someone who starts grumbling about having to learn the command line to containerize their pipeline is generally speaking on the west side of the Pareto principle. I can only guess at which particular area they will trip up, but it'll be somewhere.
antman 5y ago
Agree thar work ethic is the most important thing since complicated qualitative things cannot be measured, trust precedes everything. But work ethic does not complete the puzzle because people dont know always what they dont know. For the example you mentioned, I will use a simplification I make to explain levels of expertise of challenging knowledge: 1.ABOUT: Know about something (heard it, know some examples) 2. KNOW: Know that something well (I now understand it and can leverage it towards an end to end a useful thing, also know its weaknesses) 3. HUMBLE: Realize I did not know many things about it but now know many ways of using it, can correct and extend other people's work, most of the time. 4. EXPERT: Know why it was structured that way. Contribute to the knowledge/tool itself. So for that PhD an initial estimate would be a 3 or 4 scale on the math level, 1 or 2 on the kubernetes level (don't know him ofcourse I can be wrong without first discussing). If he works independently level 2 kubernetes is pretty great. If he needs to be part of a larger support team, a level 3 knowledge based on my (admittedly back of the napkin and ambiguous) categorization might prove to be less risky.

by urthor 5y ago

musingsole 5y ago

> I know one hard science PhD who runs their own K8s cluster at home and plays with Linux distros. That's super awesome for that data scientist, but the question for a business is can/should you structure yourself in such a way that you NEED employees with that cornercase level of joint expertise. The answer is you really can't. Individuals have awesome strengths that they developed for reasons particular to them. Use those strengths when you can. But the business has to rely on a common denominator of a role or else it'll never fill it when their unicorn leaves to go backpacking in Europe.

urthor 5y ago
Agree. You need to structure your talent pipeline, and organization, based on the average level of talent you can likely receive at your compensation bracket. You cannot create a single point of dependency on an employee who you'll never be able to replace for the same amount of money. However, the issue is that productivity is logarithmic. The unfortunate truth the school of hard knocks has shown me is that someone without the "roll your sleeves up" attitude to learn Docker is generally speaking just not going to be that effective when push comes to shove. Now if you're using tools to abstract the time of data scientists who are CAPABLE of learning Docker, that is a different story. But someone who starts grumbling about having to learn the command line to containerize their pipeline is generally speaking on the west side of the Pareto principle. I can only guess at which particular area they will trip up, but it'll be somewhere.

antman 5y ago

Agree thar work ethic is the most important thing since complicated qualitative things cannot be measured, trust precedes everything. But work ethic does not complete the puzzle because people dont know always what they dont know. For the example you mentioned, I will use a simplification I make to explain levels of expertise of challenging knowledge: 1.ABOUT: Know about something (heard it, know some examples) 2. KNOW: Know that something well (I now understand it and can leverage it towards an end to end a useful thing, also know its weaknesses) 3. HUMBLE: Realize I did not know many things about it but now know many ways of using it, can correct and extend other people's work, most of the time. 4. EXPERT: Know why it was structured that way. Contribute to the knowledge/tool itself. So for that PhD an initial estimate would be a 3 or 4 scale on the math level, 1 or 2 on the kubernetes level (don't know him ofcourse I can be wrong without first discussing). If he works independently level 2 kubernetes is pretty great. If he needs to be part of a larger support team, a level 3 knowledge based on my (admittedly back of the napkin and ambiguous) categorization might prove to be less risky.