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I think evaluating the skills is not difficult within the timespan of an interview, if done right. Let's sit down at a specific problem I am working on. Let me

I think evaluating the skills is not difficult within the timespan of an interview, if done right. Let's sit down at a specific problem I am working on. Let me tell you what I'm doing with the tools that I have. Your past work was in some way similar, and you can relate to what I'm doing in some way. Either you can jump right in and take over the keyboard, or you can tell me how you solved a similar problem with your tools and frameworks. I can show you how it's done with my setup, and teach you a few things in the process - which you will easily pick up as you have related experience. You got either questions that are exciting, maybe challenging me how I am doing it, or maybe teach me just a keyboard shortcut I didn't know, proving the time and effort you spent on this. We are both excited to learn a lot in a short amount of time and I know I want you to work with us...

Frost1x 7y ago
To me, what you mentioned is pretty obvious. A short conversation with someone can usually fuss out if they're making things up or reading a script they've memorized. It's far easier for the interviewer and the interviewee. Instead, it's all about covering yourself as the interviewer so you adopt the new garbage industry standard and pretend those are sound metrics. When the metrics are not that telling if a new hire is bad, you have an out that it's the process, not your mistake.
0-_-0 7y ago
Wouldn't your approach be biased against people who need more time to come up with a solution, and against people who are not used to discussing their solutions with others in real time? You approach also doesn't include an objective measure of performance: Your judgement of the candidate could be highly dependent on their personality, instead of their actual skills. If I recall correctly the book "Thinking fast and slow" shows how easily interviewers are influenced by personality traits. Technical questions like "how do you invert a binary tree" or "what is KL-divergence" are much less personality dependent, and the quality of the answer is measurable. Here is what I think are some important skills in data science: Can you understand the principles behind existing solutions and build a new solution from those principles? (How fast) can you understand a new approach and apply it to a problem? How long does it take you to translate a solution to working code? How optimal, readable and reusable is it?
- UweSchmidt 7y ago
  My proposal hopefully doesn't put the candidate on the spot, but allows for any and all useful contributions throughout the process, be it fast or slow thinking or anything in between. Hopefully in that hour or so where we sit down, there is enough time to overcome most of the bias that we had when the candidate walked in. Looking at your list of important skills in data science it seems we mostly agree on what is important...

Frost1x 7y ago

To me, what you mentioned is pretty obvious. A short conversation with someone can usually fuss out if they're making things up or reading a script they've memorized. It's far easier for the interviewer and the interviewee. Instead, it's all about covering yourself as the interviewer so you adopt the new garbage industry standard and pretend those are sound metrics. When the metrics are not that telling if a new hire is bad, you have an out that it's the process, not your mistake.

0-_-0 7y ago

Wouldn't your approach be biased against people who need more time to come up with a solution, and against people who are not used to discussing their solutions with others in real time? You approach also doesn't include an objective measure of performance: Your judgement of the candidate could be highly dependent on their personality, instead of their actual skills. If I recall correctly the book "Thinking fast and slow" shows how easily interviewers are influenced by personality traits. Technical questions like "how do you invert a binary tree" or "what is KL-divergence" are much less personality dependent, and the quality of the answer is measurable. Here is what I think are some important skills in data science: Can you understand the principles behind existing solutions and build a new solution from those principles? (How fast) can you understand a new approach and apply it to a problem? How long does it take you to translate a solution to working code? How optimal, readable and reusable is it?

UweSchmidt 7y ago
My proposal hopefully doesn't put the candidate on the spot, but allows for any and all useful contributions throughout the process, be it fast or slow thinking or anything in between. Hopefully in that hour or so where we sit down, there is enough time to overcome most of the bias that we had when the candidate walked in. Looking at your list of important skills in data science it seems we mostly agree on what is important...