Finding a new job can be a job unto itself: perfecting a resume, scanning career websites, networking among friends and acquaintances-the dizzying amount of work can turn the excitement of a new career chapter into anxiety. Job searches of the future are going to be a completely different experience, with technology doing most of the heavy lifting.
Conversations with several figures in the field of job search and recruitment-representing behemoths in the industry and smaller players-revealed a job-hunt landscape that will look very different just a few years from now. Though their approaches to matching worker and employer may differ, all agreed that the use of artificial intelligence (AI) and machine learning would streamline the complexity of matching work to talent and that the job search and recruiting experience of the future will involve more face time and less resume spamming.
The Job Board
About 65% of people look for new jobs within 91 days of being hired at a job-which suggests matchmaking in the search for work may be less than perfect, says Raj Mukherjee, senior vice president of product for Indeed.com, the job search platform that operates in more than 50 countries and has about 200 million visitors to its site per month.
Those imperfections can only be fixed with data-and lots of it. The sheer amount of information on job skills, salaries and user tendencies makes matching people to positions simply impossible without using AI to crunch the numbers. "We're generating roughly 25 terabytes of data every single day," says Mukherjee, "and as we do that the data becomes the foundation of how we use AI and machine learning to improve the experience that job seekers get."
AI and machine-learning allow services like Indeed to make predictive assessments of factors such as what a salary should be. Calculations hone in on what wages would be appropriate for a particular job, in a specific location, or at a specific company. Exact job title also comes into play.
Indeed's AI also pulls information from resumes for recruiters using something called natural-language processing, which involves extracting relevant words and phrases from text using computer programs. This is tricky because text data can be relatively unstructured. "It would look at a sentence and try to make sense of that sentence," says Mukherjee. "It would sort things that the sentence implies." Listed skills on a job seeker's resume would be recognized and set aside, he explained, as would companies worked for, years of experience and other elements. This information would then be compiled to make it easier for recruiters to evaluate.
As for the future, Mukherjee says job seekers will likely see a reduction in their research time while looking for work. As AI and machine-learning develop in the field, a service like Indeed should be able to suggest new, much more compatible opportunities based on a job seekers' work experience, skills, salary, interests and location. A career-focused AI should also tell job seekers whether they are being paid fairly at their current job, with a high degree of accuracy, compared to others in their line of work.
For recruiters, says Mukherjee, weeding out incompatible talent is key. That means gauging skills at the outset, which would require testing on a job search platform-saving time for both recruiter and job seeker.
Skills Above All
Skills testing is also the centerpiece for CodeFights, a four-year-old San Francisco-based firm that offers tech workers a platform on which to practice their coding skills and to use those skills-which are scored and rated on the CodeFights platform. They can connect with prospective employers, who also use the company specifically to find prospective hires. This system of skills-based recruiting opens doors for programmers based largely on their proven talents and offers recruiters a definitive look at what a prospective hire can actually do.
For Tigran Sloyan, CodeFights' co-founder and CEO, looking for work is a classic matchmaking scenario, similar to ones being facilitated in the transportation market by Uber and in the hotel and short-term rental space by Airbnb. But in the effort to connect workers and employers, complexities abound. There are so many data points to consider: skills, salaries, location, personality, experience, company culture, inaccuracies in resumes or job descriptions. "The more objective data we introduce in technical recruiting-and in recruiting overall-the better all of us will be," says Sloyan.
Sloyan foresees that job search platforms or services will ultimately become education hubs, offering skills-building services that also rate proficiency, making it easier for job-seekers to prove their quality to recruiters, with whom they would be matched. "Eventually there will be a company that's teaching the entire world," says Sloyan. "A company that's teaching the entire world is doing assessments on a vast scale, and you cannot compete with that kind of company as purely a recruiting company."
A Layer Of Testing
Assessing skills specific to various industries is the name of the game for Harver, a five-year-old firm based in Amsterdam. Its specialty is having job applicants go through a testing process-chosen by hiring companies-that gauges whether they are a good match for a given position. The firm has made deals with Netflix, Booking.com, OpenTable and Zappos.
"A lot of people apply for jobs and they don't have any clue what the job is really like," says Barend Raaff, Harver's founder and CEO. It's also impossible to predict whether someone would stay in the job and do it proficiently, he added. The firm designs algorithms and testing processes with recruiting clients to predict a good match for a job. Harver began by focusing on high-volume job sectors like call centers and customer-facing roles, and has now begun catering to smaller companies' recruitment needs. Through the application and testing process, job applicants get a sense of what to expect from a job, and Raaf says that in the future if an applicant is rejected for a job, Harver will suggest positions at other organizations for which he or she is better suited.
Raaf also says the role of recruiter will become easier as the means to gauge talent will be far more robust. The human element, he noted, will still be needed to make the final decision on hiring. "Surgeons 50 years ago had an X-ray picture, and based on an X-ray picture they decided if they would operate," he explained. Today they have the means to gather greater levels of data on what ails their patients, but the big decisions are still made by humans. "They make the call whether they will operate or not. In recruitment we are heading in the same direction."
A Deeper Knowledge Equals Efficiency
At Linkedin, AI is used to sift through job-seeker and recruiter data alike, as well as information on the company's 500 million-plus registered users, says John Jersin, senior director of product management for the company. "We've seen efficiency improvements upwards of 50% in our core products for recruiting already-the number of interactions that it takes to find a candidate that engages in the process, or reaches a certain stage, or find a candidate that gets hired."
Still, today job and talent search is inefficient, says Jersin: seekers must send out many applications to find a position and recruiters must sort through a high volume of candidates before finding one that is a fit. "The work on AI that we're doing helps us better understand what 'fit' actually means in a way that we can predict it," notes Jersin. "If we can predict it, we can make the right matches faster and the wrong matches less often."
In the future, says Jersin, job seekers and recruiters will spend less time on the drudgery of the job market and more time in face-to-face meetings-that is, on both sides, the ideal outcome-largely because AI and machine learning will make matching more efficient. But, says Jersin, there are elements to a job search that are simply incalculable by artificial intelligence. "The thing that will always matter is whether that place clicks for them on an emotional level, whether that job gives them the feeling that they are fulfilled, and whether the people there are the kind of people that they want to work with."