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What I learned from reading 8,000 recruiting messages

blog.hired.com

21–26 of 26 posts

Re: What I learned from reading 8,000 recruiting messages

#21
This was an awesome post and I really enjoyed it. I hope to see more data driven analysis like this in the future. I actually try to respond to every recruiter email I get, even if they are obviously generic (though I have my limits). My one goal is for them to give me a number. I typically thank them, ask them how they found me (specifically what keywords they searched for, etc) and what the salary ranges are for the position. I usually frame it to "ensure I am staying market competitive". I'd say 50% of the time I get the dodge one way or the other, 40% of the time they are paying below what I'd expect to be paid for the position, and 10% is what I'd expect. Only one time was I surprised by the salaries thrown out and those were positions in NYC. That said, I'm going to get a little cynical, so be warned.

Keeping salaries secret has been a huge tool wielded by hiring managers and HR in order to suppress salaries for a very long time, especially with the way engineers are typically pigeon holed in to broad titles. I don't see that changing any time soon, as much as I'd like too. It'd solve a number of problems for our profession if that were so. I'd be interested to see what the differences are from the initial offer when reaching out and what the official offer is if a candidate accepts.

I'm guessing most businesses that use this platform have figured out the optimal number to get responses from recruiting emails and then used any leverage they can get in order to discount the engineers skills and experiences in order to get them in to a lower salary. Unfortunately engineers are notoriously bad negotiators and we start to buy in to their arguments and end up accepting bad deals and we don't realize it.

But, this is definitely a step in the right direction.

Re: What I learned from reading 8,000 recruiting messages

#22
My experience (back when Hired was known as DeveloperAuction) is that most of the 'offers' I received were completely impersonal and disingenuous.

What I mean is that it seems the companies 'bid' on anyone who met some minimum requirement and relied on the initial phone screen to actually vet the candidate. The three phone screens I had the person on the other end displayed no knowledge of the experience and skills I listed on my profile and one did not even have any preference what division of the company I should work for (was just looking for another somewhat competent software engineer, not caring much beyond that)

My point is: personalized messages are more likely to result in a hire BECAUSE the person writing the message knows what they are looking for. Conversely the recruiters I deal with on a regular basis are only looking for a 'python engineer' or something similar. They could not possibly write a personalized recruiting message.

Re: What I learned from reading 8,000 recruiting messages

#23

This was an awesome post and I really enjoyed it. I hope to see more data driven analysis like this in the future. I actually try to respond to every recruiter email I get, even if they are obviously generic (though I have my limits). My one goal is for them to give me a number. I typically thank them, ask them how they found me (specifically what keywords they searched for, etc) and what the salary ranges are for th…

We've gotten 1100+ companies to disclose salaries upfront on our marketplace, which is what all the numbers in the blog post are based on...

I definitely agree that keeping salaries secret until the very last second - when you've spent days or weeks in an interview process, and become emotionally vested in the outcome - is a tool that's been wielded against the benefit of Engineers.

Our goal is to break that cycle, and shed some transparency on this otherwise opaque part of the hiring process and get better alignment upfront about comp. expectations before time gets wasted by either party.

Re: What I learned from reading 8,000 recruiting messages

#26
post #20
post #9

Earlier quoted context omitted.

Sorry, you're right, that's unclear. We chose to run a logistic regression on a subset of factors that were statistically significant (i.e. every factor in the graph is significant). In other words, we chose the factors that had the largest effect size and then plugged them into the regression.

so wait -- you ran the regression with lots of factors, and then dropped those you found not to be statistically significant? I would suggest it's not good practice to drop variables, even if they aren't statistically significant (and what an argument that can become if you test things simultaneously). Particularly if there's any chance they are correlated with other variables. Read Pearl; causality (which is what yo…

I ran a number of different regressions in parallel with significance testing. Ultimately, I chose to publish the figures from the one that included the factors with the largest effect sizes (determined in parallel) for simplicity, but the takeaways didn't really differ much when more factors (even ones that weren't significant) were included.

Regardless, thanks for pointing me to Pearl. Linking here for others in case they're interested, too: http://bayes.cs.ucla.edu/BOOK-2K/

And, yes, I'm that Aline. Ohai, and thanks!

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