What I learned from reading 8,000 recruiting messages
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What I learned from reading 8,000 recruiting messages
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Re: What I learned from reading 8,000 recruiting messages
#2Love this. I wish more recruiters were this data-driven.
Re: What I learned from reading 8,000 recruiting messages
#3Great 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? ;)
Re: What I learned from reading 8,000 recruiting messages
#4Great 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 :)
Re: What I learned from reading 8,000 recruiting messages
#5> 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).
Re: What I learned from reading 8,000 recruiting messages
#6TLDR: 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.
Re: What I learned from reading 8,000 recruiting messages
#7Love this. I wish more recruiters were this data-driven.
Hard enough for most recruiters to read a resume, in my experience.
Re: What I learned from reading 8,000 recruiting messages
#8> 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…
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).
Re: What I learned from reading 8,000 recruiting messages
#9> 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…
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.
Re: What I learned from reading 8,000 recruiting messages
#10> 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…
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.