How AI is revolutionizing recruiting and hiring

How AI is revolutionizing recruiting and hiringArtificial intelligence (AI) is shaking up the recruiting process, changing the way recruiting agencies and sourcers discover and hire tech talent.

AI and machine learning enable professionals to quickly analyze huge amounts of data and make decisions and predictions based on that, said Summer Husband, senior director of data science at Randstad Sourceright, in a presentation at the 2017 SourceCon event. Now, recruiters and sourcers are putting AI to work to help define a job posting’s “perfect fit,” better surface strong candidates from search pools, and improve their ability to fill job openings fast.

Shortening up the hiring window

Unfilled job postings are a significant drain on organizational productivity. To ensure the right recruiting resources are being applied to filling a particular job opening, some sourcers are taking a tip from the healthcare industry’s use of survival analysis.

In healthcare, survival analysis is a machine learning technique that analyzes time to an event, such as a patient’s expected time before recurrence of a disease or a death. Here, Husband sees a good analogy to sourcing and recruiting

“We take data on jobs we’ve filled for clients in the past, how long those took, how many candidates, open roles, information about the company as well as job market data from sources like the BLS and CareerBuilder, for instance, to find out how all of those things impact the ‘survival rate’ of our open jobs,” Husband said. “We’re obviously flipping the script, because we want our open jobs to die quickly, but the process is the same.”

That process allows sourcers to set reasonable expectations for clients and allocate appropriate resources to harder-to-fill roles, she said.

“Our goal is that when we see a new [job requisition], we can gather these features right away and we can see whether or not this will be a tough one to fill, and then we can decide whether we should put extra resources toward that now instead of waiting and potentially failing to deliver candidates,” she said.

Accuracy is also important; being able to evaluate the precision (the fraction of received information that’s relevant) and recall (the fraction of relevant information that’s received) of algorithms can help sourcing and recruiting professionals make sure they’re delivering the right candidates, she said.

“Sometimes, overall accuracy isn’t the most important thing. In recruiting, it’s okay if you’re missing a few people who might be a good fit. We’d rather that than send a whole bunch of bad results—or inappropriate candidates,” she said.

Reverse-engineering the ‘perfect fit’

Some forward-thinking recruiters and hiring managers are using AI and machine learning to reverse-engineer candidate “fit,” and to predict a potential candidate’s performance in the role, said Chris Nicholson, CEO of artificial intelligence software company Skymind.

“The best use case would be solving the matching problem so that you’re leveraging the tech to find the best candidate for the employer and vice versa. The question everyone’s trying to answer through all the interviews, screenings, tech and coding challenges, is, ‘How can I predict someone’s performance?’ So, the smartest recruiters and hiring managers would start gathering résumés, performance reviews, work product, any information at all about highly successful people that already work for them and plug that into an algorithm to figure out what you are looking for,” Nicholson said.