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Comment on Bayesian Inference for Hiring Engineersparent

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At least where I work, we are painfully aware of this tradeoff, but are willing to make the sacrifice because of the severe costs of making a bad hire. We are small, and cannot afford to lose 90 days.

I realize this article refutes this point of view, but finance and HR do not see it this way.

I am more happy to devote several hours of each week interviewing than entire days/weeks mentoring and reviewing code for a bad hire, or worse, dealing with the widespread consequences of a culture misfit in a small team.

Hiring is hard.

At least where I work, we are painfully aware of this tradeoff, but are willing to make the sacrifice because of the severe costs of making a bad hire. We are small, and cannot afford to lose 90 days.

Small or large, I think this is fair. But also fair is to take this into account when making claims about any alleged shortage of qualified workers.

Not that you or your company have necessarily made any such claims but plenty of companies that similarly follow a low false positive at the expense of false negatives approach have done so, including in sworn testimony before legislative bodies.

(Original article author here.)

I'd distinguish between screening false positives (which my article focuses on) and hiring false positives. Screening rejections (before the final interview) are usually done with very limited information, so there's more room for bias and noise.

Hiring is hard! :)

You're right, the difference is important.

I've been the tail end of the interview funnel when experimenting with our screening strictness. The emotional toll that less stringent screening had on my day-to-day work (faster, rapid, and high frequency on sites) was extremely high. It led to a month or two of burnout as the lead engineer on the team, despite probably leading to finding a hire sooner. The cost is, in my opinion, immeasurable.

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