What I learned from reading 8,000 recruiting messages | Hacker News Reader

What I learned from reading 8,000 recruiting messages

blog.hired.com

110 pointsleeny24 comments

Great analysis, Aline. The positive examples are exactly the kind of emails I'd like to receive. On the other hand, if all recruiters followed this advice, I'd have a harder time rejecting spammy recruiters... So maybe keep this on the down-low? ;)

Heh, I think giving advice is much easier than actually following it :)

As a developer with a fairly interesting background, I've been getting messages from recruiters for years. As Aline's post elucidates, most of the messages are completely impersonal. I had been ignoring these e-mails for years. I then got a message from a recruiter that was really well written and completely geared towards me. I was shocked that someone took the time to actually read my resume. That recruiter got a response from me. He then lined me up with a gig. His approach also got four other people hired at the job he lined me up with.

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.

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.

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.

> These values come from running a logistic regression on the factors that were most statistically significant. The y-axis represents the standardized value of the coefficients in our regression.

"Most statistically significant" does not imply that the variables themselves are statistically significant, and it does not imply that the logistic regression itself is accurate (especially since the regression uses only 5 variables). What is the accuracy of the model?

The value of the logistic regression coefficient is the log-likelihood of the estimate, not the "Significance of Regression Coefficient", which is a completely different value altogether (the p-value).

You can see the model's fit (when using only the 1st variable, relative salary) in this graph https://plot.ly/~elliotk/17 - there were more variables in the regression but only those 5 had significant coefficients. You can see the error bars on the chart (eg, "made by engineer / founder" is not really significant).

I might have mis-spoke, I think there were a couple other factors we used, but Aline is right we did a dimensionality reduction before running the regression.

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 you're really discussing) is a rat's nest.

Also, when you plot the value of the coefficients, I would suggest ordering them by abs(coef) * stddev(var). This may not matter much for you since it looks like most of your variables are indicator variables, but it's still good practice.

edit: oh, hi, you're the Aline that wrote this? Thanks for the interesting analysis.

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!

Excellent analysis. Great follow up to the http://blog.alinelerner.com/lessons-from-a-years-worth-of-hi...

Love this. I wish more recruiters were this data-driven.

Hard enough for most recruiters to read a resume, in my experience.

Since Hired is a marketplace, it's a lot easier for us to analyze data because:

1.) Data is stored in a central database, and not across dozens of email accounts, LinkedIn Recruiter accounts, etc.

2.) Working with over 1100 companies, and hundreds of candidates every week, we have a huge sample size to draw on

3.) We actually have a full time Business Intelligence Analyst on our team, and we subscribe to data analysis tools such as "Looker" to build internal dashboards, and metrics which help us drive marketplace efficiency... we also have a Data Scientist on staff, whom we found on Hired (naturally!)

This is awesome, especially as someone who is new to recruiting in the tech space. Sent to a couple other HR/recruiters and they love your posts!

Not sure I understand; the main variable for initial response is salary yet in none of the examples of initial emails salary is mentioned (that's my experience as well)

On Hired's platform, each recruiting message is accompanied by a salary offer, separate from the message body.

I hope every developer reads this.

If I ran a company, I would want to hire this woman.

She's an engineer actually fixing recruiting... What more could you want for a talent acquisition thought leader.hopefully she can bring about more evangelism to recruiting.

A lot of companies tried to hire Aline. She prefers to run her own one. See also http://blog.alinelerner.com/

TLDR: recruitment letters need to have attached in them with a competitive (at or above average) salary number, personalized to the prospective employee. In other words, actually email the person and be transparent with what you're offering.

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