Not too long ago the internet was a great place to get a job.
But lately candidates have noticed how much harder it is—and how many applications the average person has to fill out before they even get a phone screen or an interview.
We recently did a poll on LinkedIn and asked candidates why they thought it was so hard to find a job online—43% of people thought it was because of high applicant volumes, while 32% identified fake job postings as the main culprit.
Today we’re going to examine this topic in depth, and look at how technology and hiring practices have changed in a way that makes it harder to find a job. We’ll be going through many of the current issues in online job seeking, including
- Ghosting
- “Fake” jobs
- Employment practices
- The impacts of AI
So if you’ve ever wondered why so few candidates hear back from jobs, or how AI is making applying for jobs both better AND worse, read on!
Is there anybody out there? Ghosting
One very, very common candidate experience is applying for a job and not hearing back.
No rejection, no interview invite, just dead air.
And when candidates do get a response, it’s usually an automated email, sometimes weeks later, that doesn’t tell you anything about why you didn’t get the job.
Often employers aren’t doing this on purpose—usually their application volumes are so high that they don’t have time to send anything other than automated responses. So whether you get no response or a form email, the result is usually the same—your application has been lost in the pile.
This can be very frustrating when candidates apply to hundreds of jobs—not only are you not getting anywhere in your job search, but you’re also getting ZERO feedback on why you aren’t getting interviews, what might be wrong with your resume, or what is missing from your education and work experience—it even makes people start to wonder if any of these jobs are even real.
Which brings us to…
“Fake” Jobs
There is a growing idea that some, or maybe even many, online jobs are not legitimate. People point to jobs that are open for multiple months, or jobs that get taken down and put back up every couple weeks, as evidence.
Generally, “fake” jobs could refer to any of the following:
- Jobs where employers already have a candidate in mind but advertise anyways (usually an internal promotion)
- Jobs meant to collect candidates for a future opening rather than a current one
- Jobs that gather market data for use in an internal promotion or salary raise
- Scams trying to collect people’s personal or banking info
It’s hard to say how much of a problem “fake” jobs really are—only the person posting the job knows if it is a real job there or not. But according to Forbes, up to 36% of job postings won’t have a real job available.
When you combine that with the negative candidate experience—applying to dozens or hundreds of jobs without hearing back—it’s easy to see how people would think that many jobs aren’t even real.
The Employer Perspective
Employers have a role to play in all this too, though recent changes in the job market are making it difficult for employers to keep up with all the applications they receive.
Most online jobs—particularly remote roles—will have hundreds, even thousands of applications.
Using human labour to go through all those applications would be impossible, so most employers use data parsing and keyword searches to identify promising resumes. From those, recruitment teams will do phone screens to check some basic knowledge and skills before sending them on to a formal interview.
And when it comes to contacting all the people who applied but didn’t make it through the keyword search, there isn’t much feedback for employers to provide—all they can do is send out an automation thanking you for the application and saying they went with a different candidate.
Which brings us to…
AI Disruption
“Somehow, companies still think that AI tools for screening and ranking are going to function like the world still exists where candidates spend days writing an accurate resume still. The very concept of stack ranking anything goes out the window if resumes are written to match job descriptions to make sure they get noticed.
It’s like we all forgot that resumes are an interview tool, not a sorting mechanism. They aren’t built to allow for screening because they are user generated data, not structured data. When we made the decision to rank resumes with algorithms, we taught the market to change their user generated data into structured data to defeat the very algorithms we now rely on.” – Jim Durbin, “The Top talent Counterpunch”
It used to be that candidates had fairly straightforward resumes—they weren’t engineered for any particular job or company, they just stated, in simple terms, what the candidate’s experience and skills were.
That’s what Jim means by unstructured data—the resumes weren’t built for any particular system, which meant that sometimes recruiters had to “dig” a little bit to find relevant experience that could be good for a job—they would “read between the lines” and try to figure out if this person could do the job.
There is a fair bit of skill in this—finding hidden gems, or dodging bad applicants that looked good, were hallmarks of successful recruiters who had to interpret that data in a resume rather than just verify it.
But with more technology came more applications, and recruiters didn’t have the bandwidth to “dig” through resumes at the same level of detail. Instead, they had to rely on technology to streamline the process, starting in the early 2000s, where keyword searching was popularized during the boom of online job boards like Indeed.
Today, the problem is much worse with AI, because resumes are overwhelmingly structured for the purpose in which they are being used. It’s like a student taking a multiple choice test when they’ve been given the answers in advance. All of a sudden, you’re no longer testing the student’s knowledge, but instead their ability to memorize, copy, and paste information.
The same thing applies in keyword screening—if everyone has all the right “answers” in advance (keywords and experience from the job posting), you aren’t screening for job qualifications anymore—you’re screening for their ability to optimize their resume for keyword scans and data parsing.
Of course some of that keyword testing is important—5+ years of experience in a certain technology could be critical to doing the job. But during the initial screen, recruiters aren’t actually testing that skill—they’re verifying the existence or absence of words on a page. And if someone’s actual skills differ from what an algorithm is expecting to see, qualified candidates can get left behind.
What can candidates do?
Focus on employers that you actually WANT to work for
While AI might encourage us to apply faster and more broadly, the opposite could also be true—identifying companies that you are aligned with, and are a good general fit for your skills and experience, can help you make a higher quality connection rather than a higher quantity.
Networking certainly helps—but even identifying and reaching out to companies where you want to work can help save you from wasted hours of online applications.
Work with a recruitment firm
Particularly for folks with specialized or desirable skill sets, working with a recruiter can make your life a lot easier.
Recruiters have access to dozens of jobs at once, and when you apply, you could be considered for multiple roles at once.
Wrap
Today there is a lot of friction in online job seeking but not a lot of answers. It’s clear that technology is posing both opportunities and challenges—both for jobseekers and employers.
If AI can start bridging the gap between candidates and employers by providing them feedback and insight into their applications that would be a great step forward. Because if candidates continue to experience difficulty finding a job online, it’s possible that online job boards may stop providing enough value to candidates to be worthwhile.
