'Needle in the haystack' : Identifying the best candidates
If you have advertised your latest job opening on the right channels, you’re likely to end up with hundreds of promising CV’s. But the more applicants you get, the harder it is to sift through to find the best candidates for the role.
Attracting large numbers of new applicants might be a sign of an attractive employer brand and a smartly written job advert, but it can slow down your hiring. Recruiters tend to take shortcuts and search on keywords, such as previous companies or job titles, but this can lead to filtering out great profiles who have excellent transferable skills and experiences but might not “tick boxes.” Often these “filters” are a sign of conscious and unconscious biases: for example, if we are looking for a strategy expert, surely Oxbridge or Ivy league graduates are a good place to start. In order to fight these biases and increase their diversity, many companies have famously dropped the hard requirements for a University degree altogether.
This simple four step process to structure the CV sifting will help you to create your own process for selecting CV’s:
Start with identifying the minimum requirements. These are the must haves, core requirements that enable the candidate to perform the role successfully. They can be qualifications (eg certified accountant) or critical hands-on experience (eg managing a team )
Add some “nice-to-haves”. While their absence isn’t necessarily a deal breaker, they provide a fast and effective way to identify top candidates to interview. Product Management experience might be a minimum requirement, experience in startups as a product manager can be a nice-to-have.
Look for clues on the candidate’s strengths and motivation.Have they adapted the CV to match your job advert, how is their attention to detail (eg, any spelling mistakes?), are they able to summarise effectively or are you struggling going through 6 pages of War and Peace?
Build a long list vs a short list: it is a candidate market and good profiles have options, so make sure you don’t put all eggs in one basket.
’Close the loop’ and provide feedback to those who didn’t make the long/short list. Even automated responses like “Thank you for your interest but..” are better than no reply at all. According to research by Linkedin, 94% of candidates want to hear feedback after an interview, and they’re four times more likely to consider a future opportunity with your company if offered constructive feedback. LinkedIn provides some great guidance on how to create a positive rejection process.
The sifting process can be automated and, whilst it can potentially improve recruiters’ efficiency, this isn’t without risks. You might overlook highly qualified candidates who were not good at structuring their CV based on keywords. You may also continue to hire against the same profiles you always have and limit diversity, since the algorithms are built on your baseline data. Most Applicant Tracking Systems have filtering options and the more advanced AI based selection software market is on the rise.
This case study explores how artificial intelligence is being put to use to help screen and assess the more than one million people per year who apply for jobs with Unilever.
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- What do you think are the opportunities and risks of a fully AI-powered CV screening process?