Most organizations use a really flawed methodology to hire people in the first place (some combo of phone scree, behavioral, technical screen, team interview). Whether you know the area you're interviewing for or not... you aren't going to figure out if someone knows their shit in 3 hours of talking to them anyway because the only way to know for sure if someone can do something is to have them actually do that thing. Unfortunately most companies don't like this concept (this is not a reference to take home projects...)
Assuming you want to skip actually working with the prospect and you want to try to assess via an interview format, you're looking for one thing: Will this person help your company make money? I.E. will they get results.
While you can never answer that question for sure, looking for human beings that are results oriented is a reasonable first step. Machine Learning as an example is typically a project based type of work. Ask about their projects, how it went, and what was the result. If you talk to enough people about the results of their work, someone will stand out (maybe many people)... then you can move along to what normal hiring managers do anyway: hire who they like the most or pick out of a hat.
The other thing you can do is get expertise in the area.
Thanks jppope. I like the idea to talk about 'results of the work' and you can ask probing questions about why it worked. If they can describe logically what they did and how it linked to those results, describe what they are certain and uncertain about it will probably be hard for a liar to fake all of that (the liar will be confident of everything).
"Results" is a funny thing to define though. For example say I know nothing about coding, and I hire someone to make a website. Based on their "results" they make good looking fast websites without bugs. I hire them. I get my site. I need a change and suddenly they are not available or got expensive. I ask someone else to do it and the say "maaan, wtf they have used Perl and a weird JS library for the front end, and purescript and I don't know how to maintain that. I'll need to rewrite it".
With machine learning, to evaluate the results, I think you'd need to know a bit about machine learning and what P(good work by this person | company made money from the model) is vs. P(null | company made money from the model)
> The other thing you can do is get expertise in the area.
This seems absurd at first, but it's a good idea in general. If my work involves hiring someone who does X, I should definitely have enough knowledge of X to make decisions on that. It doesn't have to be enough knowledge to implement X myself... but enough to make informed choices.
...basically having a "Steve jobs" level of understanding. (Steve was known to deeply understand technical concepts but without actually knowing how to code)
Similar to how I'd interview someone who is smarter than me in my area of expertise.
Try to flip the roles. Treat the applicant like a consultant/mentor. Tell them about actual problems that you need solved. See how they solve them or improve on your solution. Compare between different applicants.
You'll want to look for clarity in their solution. Someone who overcomplicates their solution is probably bullshitting you. Someone who slows down and explains everything clearly probably understands what they're talking about. Like someone who says, "I would implement ABC here" and doesn't explain the advantages and disadvantages of ABC likely has weak knowledge.
With some of the smarter people I've worked with, they make no assumptions, and establish terms. "Are you familiar with ABC? No? Well, you know A? A is flawed for this solution because ___, and so we use ABC instead of A, even though ABC has this disadvantage over A."
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[ 4.1 ms ] story [ 22.7 ms ] threadAssuming you want to skip actually working with the prospect and you want to try to assess via an interview format, you're looking for one thing: Will this person help your company make money? I.E. will they get results.
While you can never answer that question for sure, looking for human beings that are results oriented is a reasonable first step. Machine Learning as an example is typically a project based type of work. Ask about their projects, how it went, and what was the result. If you talk to enough people about the results of their work, someone will stand out (maybe many people)... then you can move along to what normal hiring managers do anyway: hire who they like the most or pick out of a hat.
The other thing you can do is get expertise in the area.
"Results" is a funny thing to define though. For example say I know nothing about coding, and I hire someone to make a website. Based on their "results" they make good looking fast websites without bugs. I hire them. I get my site. I need a change and suddenly they are not available or got expensive. I ask someone else to do it and the say "maaan, wtf they have used Perl and a weird JS library for the front end, and purescript and I don't know how to maintain that. I'll need to rewrite it".
With machine learning, to evaluate the results, I think you'd need to know a bit about machine learning and what P(good work by this person | company made money from the model) is vs. P(null | company made money from the model)
This seems absurd at first, but it's a good idea in general. If my work involves hiring someone who does X, I should definitely have enough knowledge of X to make decisions on that. It doesn't have to be enough knowledge to implement X myself... but enough to make informed choices.
Try to flip the roles. Treat the applicant like a consultant/mentor. Tell them about actual problems that you need solved. See how they solve them or improve on your solution. Compare between different applicants.
You'll want to look for clarity in their solution. Someone who overcomplicates their solution is probably bullshitting you. Someone who slows down and explains everything clearly probably understands what they're talking about. Like someone who says, "I would implement ABC here" and doesn't explain the advantages and disadvantages of ABC likely has weak knowledge.
With some of the smarter people I've worked with, they make no assumptions, and establish terms. "Are you familiar with ABC? No? Well, you know A? A is flawed for this solution because ___, and so we use ABC instead of A, even though ABC has this disadvantage over A."