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How AI, machine learning will impact tech recruiting

How AI, machine learning will impact tech recruiting

You're already reaping the benefits of AI and machine learning when you watch TV, get a ride to the airport or shop for clothing -- here's how the technology can help you recruit hard-to-find IT talent.

Artificial intelligence and machine learning already make a huge impact on the way we watch movies and television, shop, and travel, but how will these new technology advancements affect you as a sourcing or recruiting professional?

It all comes down to being able to quickly analyze huge amounts of data and make decisions and predictions based on that, says Summer Husband, senior director, data science, at Randstad Sourceright, in a presentation at SourceCon, in Anaheim, Calif, last week.

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Survival analysis

In a healthcare setting, Husband says, algorithms can be used to perform survival analysis, 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.

This process was developed for medical situations and it's a good analogy to sourcing and recruiting except instead of survival analysis, job posting data is examined. Ultimately, the goal is to answer the question, "what's the time to fill?"

"So, 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. We're obviously flipping the script, because we want our open jobs to die quickly, but the process is the same," Husband says.

That process allows recruiters to set reasonable expectations for clients and allocate appropriate resources to harder-to-fill roles, she says. "Now, our goal is that when we see a new req [requisitions], 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 says.

AI and machine learning technology also can help determine how and when sourcers and recruiters need help with their workloads by looking at who has a disproportionate share of medium- to high-risk requisitions that might take extra time or resources to fill, Husband says.

"When we call something a high-risk req, we see that 85 percent of the time those miss their target time-to-fill. So, we can see who has a heavier load of these kinds of reqs and then make decisions about what to do. Do we need to shift these workloads around? Do we need extra sourcers and recruiters working on these?" she says.

Accurate results

Accuracy is also important to being able to evaluate the precision of algorithms can help sourcing and recruiting professionals make sure they're delivering the right candidates, Husband says.

However, she adds that accuracy isn't everything. "Sometimes, overall accuracy isn't the most important thing. In recruiting, it's OK 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 says.

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Three types of searches all return results

These technologies can also help address the issue of language and semantics -- what Glen Cathey, senior vice president of global digital strategy and innovation at Randstad, describes as "the moment you realize that all searches work," and you're staring at a bunch of candidate leads without knowing the probability of their being "the right fit."

"As recruiters, what problem are we trying to solve? We're trying to find 'the best people.' That's easy to say, but it doesn't really translate into a traditional Boolean search. What do 'the best people' look like? What does 'the right fit' look like?" he says.

Cathey compares it to the Where's Waldo? Series of puzzle books; it's not difficult to search anymore, what's of greater importance now is a data problem, he says, and that's where semantic search, conceptual search and implicit search comes in.

Semantic search seeks to understand a searcher's intent and the context in which a search was performed to improve the relevance of results. Conceptual search doesn't require a precisely worded query, but just a few keywords around which to form a concept. Implicit search pushes information and results to you based on information already assumed or gathered, much like how Google automatically pushes restaurant recommendations in your local area, or pops up traffic advisories when you walk out the door to commute to work.

"You can type in a one-word search, and it'll work. It will return results. But you're looking for skills, experience, culture fit, soft skills who probably are within a specific geographic range, that are affordable in the compensation range you have, and who also is interested in the job. How are you going to sort through these results and find that person when so many look similar?" he says.

The language problem also rears its head when, at the word and/or phrase level, organizations, candidates, sourcers and recruiters might use different terminology to describe job titles, roles, responsibilities and goals, Cathey says.

"Say you're searching for a 'web developer.' If you are using a standard keyword search, you're getting results! The search works. You can fill jobs with that search. But you're only returning candidates who use that exact verbiage to describe what they do. Would every person in that role use that language? Maybe not. How do you find great people who excel at that but use different language? How would they say it? What wording can I use to identify them?" Cathey says.

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Dark matter results

They refer to these types of great candidates who use different language in their resumes and applications as "dark matter" results, Cathey says. There are more results out there, you're just not seeing them. And this creates a problem that many sourcers and recruiters don't want to own up to. "If we're really trying to find the best candidate, though, then you're excluding people with those searches. Doing it this way means you're looking only for the best of the easiest candidates to find. And that's hard to admit, right? But that's what is happening here," he says.

Focusing on the human element

AI and machine learning are evolving at a fantastic rate, and they're a great tool to add to a sourcer's or recruiter's toolbox. By speeding up the process and ensuring more relevant results, sourcing and recruiting pros can focus on connecting with candidates, engaging with them and getting them hired, Cathey says.

"Especially with some of these search techniques, even if I run out of specific people I can directly identify who would be the right person for a job, I can find people with a high likelihood of knowing the right kind of person. And that's where the human element of this profession comes in. Instead of focusing on unbillable research, you can use AI to automate these mundane tasks to allow you to focus on your clients. So, instead of focusing on finding people, you can focus on recruiting people," he says.

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