Are these conversations sound familiar?

–      “Why have you called me for Java Middleware Position? If you read my resume you can find I am primarily a Database Developer!” (reaching out to wrong candidates?)

–      “I haven’t actually worked on that skill. I just attended one training session on that technology” (relying only on keywords in a resume?)

–      “I am sorry I cannot join you as my company has decided to retain me. They have matched the offer!” (yeah it hurts when someone ditches you …on the last moment!)

–       “You have rejected 25 candidates so far. What kind of candidates are you looking for?” (recruiter asking hiring manager?… Annoyed …both!)

And so on. So where is the problem?

Organizations or recruitment agencies would source majority of resumes from traditional job portals like Monster! Such portals provide search primarily based on the keywords.

Mere presence of keyword may not tell you if that’s one of candidate’s hard skills; even if that keyword appears multiple times in a resume! Every Java guy probably put SQL (pl/sql) as a skill he worked on but would he call himself a DB developer? (What man! … Red Face!)

Just because it is Red, it need not to be an Apple! You need more than that. And that’s why we need to look beyond basic information.

In an interview, we do not judge a candidate based on answers alone. We also want to find if candidate is a right fit to our team’s culture. If he/she has the right attitude! There are many parameters on which we evaluate candidate in an interview. Just like that searching just for keywords in a resume won’t suffice. We need to evaluate resume on multiple parameters. Basically we need to get better at pre-screening of resumes.

Fields such as Artificial Intelligence, Data Science, Machine learning (ML), Predictive Analytics help you to go beyond that basic evaluation. ML principles can provide that human aspect of prescreening resumes.

Can it help to predict if candidate would ditch you last moment! May be Yes.

We at CVViZ (www.cvviz.com) are trying to solve many such problems related to recruitment. We are not perfect but we are getting better!


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