One day this fall, Ashutosh Garg, chief executive of a recruiting service called Eightfold.ai, turned up a résumé that piqued his interest.

It belonged to a prospective data scientist, someone who unearths patterns in data to help businesses make decisions, like how to target ads. But curiously, the résumé featured the term “data science” nowhere.

Instead, the résumé belonged to an analyst at Barclays who had done graduate work in physics at the University of California, Los Angeles. Though his profile on the social network LinkedIn indicated he had never worked as a data scientist, Eightfold’s software flagged him as a good fit. He was similar in certain key ways, like his math and computer chops, to four actual data scientists whom Garg had instructed the software to consider as a model.

The idea is not to focus on job titles, but “what skills they have,” Garg said. “You’re really looking for people who have not done it, but can do it.”

The power of such technology will be immediately apparent to any employer scrambling to fill jobs in a tight labor market — not least positions for data scientists, whom companies like Google, Facebook and Amazon are competing to attract.

Thanks to services like Eightfold, which rely on sophisticated algorithms to match workers and jobs, many employers may soon have access to a universe of prospective workers — even for hard-to-fill roles — whom they might not otherwise have come across.

And while that could also help some candidates, there’s a potential downside for job seekers: Such algorithms may also lower wages in these fields, said Bo Cowgill, an economist at Columbia University who has studied the use of artificial intelligence in hiring.

“You get the more nontraditional, equally qualified, equally high-performing people,” Cowgill said. But the employer “doesn’t seem to have to compete for them as much.”

For years, employers and online intermediaries have used algorithms to help fill job openings, but their methods were often crude. A computer would identify key words on résumés, then determine whether those words corresponded to text in job descriptions.

While this approach can be more efficient than manually searching a site like LinkedIn, it has drawbacks. Applicants can game the process by larding their résumés with terms the machines are likely to be looking for. Conversely, poorly worded job listings could cause computers to overlook qualified candidates.

But recent advances in a form of AI known as deep learning have made the machines used by some companies, like Eightfold and online job hub ZipRecruiter, far more powerful. Instead of simply scanning words on a page and matching them to words in a job description, a machine can now identify skills and aptitudes that don’t explicitly appear on a candidate’s résumé.

To illustrate, Garg points out that about 90 percent of software engineers at the financial software company Intuit in Mountain View, Calif., know the programming language Java, according to data Eightfold has analyzed. That means the machine is on safe ground inferring that an Intuit software engineer knows Java, even if he or she doesn’t list the skill on a résumé.

By performing a similar exercise across an entire résumé, the company’s software can build a detailed profile of a job applicant. It can extract more than the usual amount of information from categories like education (certain disciplines at less prominent universities, like natural-language processing at the University of Massachusetts, Amherst, produce high-achieving workers) and even hobbies (chess players tend to be good at coding, basketball players at sales).

Eightfold, which is based in Silicon Valley, also makes a clever trade with clients: to improve its results, Eightfold asks clients to use its human-resources software, which imports employee data in anonymous form. This includes information on how workers with different backgrounds perform in different jobs, and how much they earn.

Eightfold can then use this data to better predict performance — say, how well an Intuit software engineer who plays chess and graduated from the University of Massachusetts might do at Amazon or Microsoft.

Intuit did not respond to a request for comment.

The software tool can be especially powerful to an employer intent on expanding a search beyond candidates with conventional experience and qualifications. In that case, a recruiter can specify criteria (like industry and location, and even how likely the candidate is to accept an offer) that would turn up less traditional résumés.

“We have gone ahead and analyzed tens of millions to hundreds of millions of profiles of people to see how people have moved in their career,” Garg said. “You can predict what they’re likely to do next very easily.” He conceded that the approach worked less well in new or obscure fields with limited data.