Semi-automated Resume Screening

A few days ago, my HR recruiter and I held a conference call with several new recruiting firms from whom we wanted to source resumes. I briefed them in some detail about my hiring process and then opened the floor for questions. In addition to the expected but important questions trying to better gauge the relative importance of different skills, I was asked about some of the subjective things I was looking for in a candidate. Now, when I say subjective, I don’t simply mean hidden biases I carry around; rather, these are the qualities and experiences that I–subjectively, I suppose-believe will help a candidate succeed.

When I’m screening a candidate’s resume, I read it in MS Word so I can use the highlighter and commenting capabilities. I work through each line (using a checklist), adding comments both plus and minus, as well as capturing any questions I’d want to go back and ask the candidate if we do continue with them. I also use a tool that I wrote to scan through the resume looking for particular phrases that are grouped into categories. One of the categories is called: “Interesting”. The interesting category consists of phrases that pique my interest. A historical review of resumes shows that a low Interesting score highly correlates with rejection. Another category is called: “Words of Concern”, which, as you’d expect, consists of words that I don’t want to see in a resume. The list varies by position, but, I don’t want to see words like lead or managed in an engineering resume (unless looking for an engineering lead or trying to replace myself). “Participated in” has a special place in my doghouse. All of these words are counted up, and then a score is derived for each category and weighted by the document’s length.

In practice, the automated tool helps me do a better job by forcing me to understand each of the scores, which requires that I read through carefully. I can’t, for example, simply write off a candidate as uninteresting if they score in the 90th percentile for Interesting! The data is kept in Excel, and I color-code candidates as they get to the different steps in the process, so it’s easy to see a sizable number of candidates and compare the current candidate to past favorites.

I started out running the numbers after reading the resume, but, as I’ve become more comfortable with the output, I’ve switched the order. All resumes are still read, but if a candidate receives poor scores, then I alter my method. I’m working on better organizing my checklist so that I can tailor it to the scores. I think I want to read a low-score resume with a subset of the checklist; only if I’m convinced it’s worthwhile going back over it would I then go back and process it for the remaining checklist items.

The goal is to be as thorough and fair as possible, but also being smart about my time.

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About jeffmershon

Director of Program Management at SiriusXM.
This entry was posted in Management, Software. Bookmark the permalink.

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