This is the one thing I wish I could teach interviewers about interviewing – Gayle Laakmann McDowell
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This is the one thing I wish I could teach interviewers about interviewing – Gayle Laakmann McDowell
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Signal. Signal, signal, signal. This is what all interviewing, of all types, ever, is about. Signal refers to what you’ve learned about a candidate and what that indicates about their potential job performance. Instead, what many interviewers look at is whether the candidate gave a “good” answer. Let me explain the difference. Scenario A: You ask Alex a problem solving question. Alex develops a solution which is good enough for the real world, but he/she is unable to develop a better solution. Would you hire Alex? Many interviewers say “yes”, because their solution was good enough for the real world. But that’s actually irrelevant. When you’re hiring, you’re trying to predict who will be good on the job. If Alex’s inability to get to the better solution reflects poor problem solving skills, then that is what matters. The *signal* is weak problem solving skills. Scenario B: In your cultural values interview with Bailey, it becomes clear that Bailey’s decision-making prioritized money over the customer experience. This directly conflicts with your company’s primary cultural value: “put the customer first.” Is this a no-hire? Not necessarily. It’s true that Bailey acted in a way that is inappropriate at your company. But the question is: Why did Bailey do this? What are you actually learning about Bailey? If Bailey’s company prioritized money over the customer experience, then it’s quite reasonable that Bailey acted in this way. In fact, I would be concerned if Bailey *did* put the customer first, as this shows a refusal to operate according to company values. So what Bailey did was a “bad” thing (by your company’s standards), but the *signal* is: “Bailey understands and operates according to Bailey’s company values.” That’s a good thing. This signal piece also has significant implications on what makes a good interview question. Many interviewers want their interview question to be “like stuff you do in the real world.” It’s not harmful to have realistic questions, but that’s not the primary metric either. The primary metric is whether the question is predictive about job performance. Does it show good signal? Suppose you’re interviewing new grad candidates for a software engineering job that uses Java and a bit of SQL. What kind of SQL questions would you ask? Most interviewers go with questions about SELECT, JOIN, etc. Sure, that’s stuff you do in the real world. It’s also stuff that a reasonable software engineer could learn in a few days. Knowledge about this topic isn’t predictive of job performance, despite how “real world” it is. What this means is that, generally speaking (there are some exceptions — I can get into those in the comments), an interviewer should never require “basic knowledge” of a topic. Either you want real expertise, or you shouldn’t care about it at all. So, signal. THAT is what all interviewing is about. Oh, and the candidate experience too. You need to treat candidates with respect and transparency. If a candidate doesn’t like their experience or the people they met with, they’re going to decline your offer *and* tell their friends (or strangers online) to not interview with you.Gayle Laakmann McDowell is the founder/CEO of CareerCup.com and the author of the best-selling Cracking the *interview books (Cracking the Coding Interview, Cracking the PM Interview, and Cracking the Tech Career). She consults with large and small companies on their engineering hiring process and teaches interviewer training workshops to improve hiring effectiveness. She previously worked as a software engineer at Google, Microsoft, and Apple. She can be found online at twitter.com/gayle, facebook.com/gayle, and gayle.com.
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read these next
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Office Hours: I'm Heather Hartnett, General Partner and CEO at Human Ventures. Our investments include Reserve, TheSkimm, and Paloma Health.
Office Hours: I have founded 3 biotech companies, finished my PhD by age 23, and teach AI at Stanford. I'm Sohila Zadran.