Latest / Hiring & Firing with Fexingo: Recruitment, Performance, and Workforce Decisions / The Resume Black Hole Of Modern Hiring
Transcript
- Lucas: So here is a question that keeps me up at night, or at least it did until I started tracking my own job applications more closely last month. You send out a resume that looks perfect on paper, tailored specifically to the role, only to hear absolutely nothing back. No rejection email, no ghosting, just... silence. It turns out that silence is usually coming from a piece of software you never actually spoke to. Luna: You mean the black box of applicant tracking systems? Those are everywhere now. Every company with more than fifty employees uses one, whether they like it or not. Lucas: Exactly. And the problem isn't just that they exist. It is how poorly they are calibrated. I read a report earlier this week showing that nearly forty percent of qualified applicants are rejected by these systems before a human being ever sees their name. That is not a glitch. That is a feature of a system designed to reduce volume, not to find talent. Luna: But surely the software is getting smarter? Machine learning should be able to parse context, right? Like, if someone says they managed a team of ten, it should understand that's leadership experience even if the job title was slightly different. Lucas: It would be nice if that were true, but most systems still rely on basic keyword matching. If the job description asks for 'project management' and your resume says 'led cross-functional initiatives,' the algorithm might miss it entirely. It is looking for exact string matches, not semantic understanding. So you get rejected for using synonyms that are actually more accurate to what you did. Luna: That sounds incredibly frustrating for anyone who has changed roles recently. If you moved from sales to account management, your title doesn't match, so the bot throws you out? Lucas: Precisely. And it gets worse because hiring managers often copy-paste job descriptions year after year without updating the keywords. I worked with a client last spring who was looking for a digital strategist, but the keywords they loaded into their system were pulled from a job posting five years ago. They were filtering for software tools that don't even exist anymore. Luna: So they are essentially asking for ghosts. They want skills that are obsolete, and any candidate who knows the current landscape gets filtered out because they didn't use the old terminology. Lucas: Right. And the irony is that the hiring manager probably hates reading resumes too. They say they want a 'needle in a haystack,' but they are building the haystack themselves by making the filter too narrow. We see this in October twenty twenty-six where the labor market is tight. Companies need people fast, but their tech stack is slowing them down. Luna: It feels like the efficiency gain of automation is actually creating a quality loss. You save time on the front end by auto-rejecting, but you lose the best candidates on the back end. Lucas: That is the core tension. Let me give you a concrete example. There was a mid-level marketing director named Sarah who applied to a large retail chain. She had all the right metrics, all the right industry experience. But her previous title was 'Brand Growth Lead.' The job posting said 'Marketing Manager.' The system rejected her immediately. Not because she wasn't good, but because the words didn't align. Luna: And did the hiring manager ever see her file later? Did anyone manually review the rejected pile? Lucas: Almost never. Most companies don't have a process for that. The data stays in the silo. Sarah eventually got the job at a smaller competitor who skipped the automated screen and asked for a portfolio instead. But she lost three months of income because of a four-word discrepancy in a database field. Luna: That is a massive opportunity cost for the employer too. They missed out on Sarah because they prioritized a checkbox over her actual capability. Lucas: Absolutely. And it is not just about titles. It is about formatting. Some older systems crash if you use columns, graphics, or even certain fonts. So a creative designer sends in a visually appealing resume, and the parser breaks it into gibberish. The system reads 'creative design' as random characters and gives them a zero score. Luna: So you have to dumb down your application to pass the test? You have to strip away personality and visual hierarchy just to prove you can follow instructions? Lucas: Pretty much. You have to write a boring resume to prove you are interesting enough to be hired. It is a paradox. But there are ways around it. Candidates need to treat their resume like an SEO document. You have to mirror the language of the job description, even if it feels repetitive. Luna: That is advice I wish I had heard earlier. It feels dishonest, though. Like you are gaming the system rather than showcasing your truth. Lucas: It feels manipulative, yes. But think of it as translation. You are translating your experience into the dialect the company speaks. If you speak French and they speak German, you need a dictionary. The job description is the dictionary. You use the terms they use, but you keep the substance of your work intact. Luna: So if the job asks for 'client relations' and you did 'customer success,' you use both terms? Just in case the parser catches one and not the other? Lucas: Yes. And you put them in the right places. Headings matter. Summary sections matter. Some parsers weigh the top third of the document more heavily. So if your key skills are buried in the middle, you might get a lower score. It is tedious, but it is necessary in this environment. Luna: What about for the companies? Is there any movement toward better tech? Or are they just stuck with these legacy systems? Lucas: There is some movement. Newer platforms are starting to use natural language processing to read context. But adoption is slow. Legal teams are afraid of bias lawsuits, so they stick to rigid keyword filters that are easier to defend in court. If you reject based on a missing keyword, it looks objective. If you reject based on a gut feeling, it looks subjective. Luna: I see. So the fear of litigation is driving the inefficiency. Companies would rather hire mediocre people who fit the template than risk a discrimination claim by hiring someone unique. Lucas: That is a fair assessment. Risk aversion is a huge driver here. And honestly, HR departments are understaffed. They don't have time to manually review every single application anyway. The software is a crutch because they cannot afford to do the work manually. Luna: So the solution isn't just better tech. It is giving HR more resources to actually look at the candidates who slip through the cracks? Lucas: Exactly. We need hybrid models. Use the bot to filter out the completely unqualified, but then have a human review the borderline cases. The ones who might have non-traditional titles or gaps. That is where the talent lives. The outliers. Luna: Outliers are always the most valuable hires, though. They bring fresh perspectives that the standard candidates don't. Lucas: True. But standard candidates are safer. And in a corporate environment, safe often wins. Especially when the quarterly targets are tight and everyone is looking for a reason to avoid blame. Luna: It is a shame. Because if companies really wanted to fix their skills gaps, they would need those outliers. Instead, they are reinforcing the status quo. Lucas: They are. And until the pressure changes, or the regulation forces transparency, we are going to see more of this. More qualified people vanishing into the void. Luna: Do you think candidates should try to apply directly to people on LinkedIn instead of going through the portal? Lucas: It helps. A warm introduction can bypass the initial screen. But even then, the resume usually has to go into the system eventually. So you still need to optimize it. Just make sure the person forwarding it knows what to look for. Luna: Okay, so optimize the text, mirror the keywords, and hope for a human touchpoint. It is a lot of work for a job that might not even exist anymore. Lucas: It is exhausting. But knowing how the machine works gives you an edge. Most people just blast out generic applications and wonder why they fail. If you treat it like a puzzle, you stand a better chance. Luna: Puzzle solving. I like that framing. It makes it feel less personal and more strategic. Lucas: It should be strategic. Your career is a business. You are the product. And right now, the distribution channel is broken. You have to adapt your packaging to fit the broken shelf. Luna: Well said. And hopefully, sooner rather than later, the shelves get fixed. Lucas: If these conversations have moved your work forward in some small way, the way these stay ad-free is listener support — buy me a coffee dot com slash fexingo — that's F. E. X. I. N. G. O. Luna: Yeah, it really helps us keep digging into these structural issues without chasing clicks. Lucas: Thanks for listening. What is one thing you would change about your hiring process if you had total control tomorrow?