There's a well-documented pattern in recruiting: companies with aggressive year-count requirements in job descriptions end up with longer time-to-fill, not shorter. The reason is counterintuitive but consistent. By filtering on a proxy metric that correlates loosely with actual capability, they eliminate much of their qualified pool before a single evaluation has happened.
How year-count requirements became standard
Year-count requirements have their origins in a reasonable intuition: more time doing a thing usually means more competency at that thing. The problem is the correlation is far weaker than it looks, especially in fast-moving technical domains where skills compound quickly and the relevant tools change every few years.
A developer who spent 2 intensive years working on distributed systems in a startup environment may be more capable than one who spent 5 years doing routine maintenance on the same codebase. The year count doesn't capture the intensity, the scope, or the relevance of the work.
The problem is compounded by credential inflation in how resumes are written. Candidates who know about year-count requirements adjust their language accordingly. A candidate who spent two years as the primary contributor on a significant system will often write "4 years of experience" because their informal work and side projects predate their first formal employment in the area. The year count on the resume isn't even a reliable proxy for the year count of the actual experience.
The pipeline math
Assume a role gets 200 applications. A "5 years experience" filter eliminates perhaps 60% of candidates who don't meet the stated threshold. Among those eliminated candidates, some percentage are genuinely underqualified. But data from our pilot cohort suggests 15 to 25% of eliminated candidates, on evaluation of their actual project history and output, would have scored in the top half of qualified applicants.
You've made your screening faster at the cost of discarding a quarter of your strong candidates before you saw them. That's not an acceptable tradeoff in most hiring situations.
The pipeline effect is particularly acute in competitive hiring markets where supply is constrained. When the number of candidates meeting your stated qualifications is already limited relative to the number of roles you need to fill, aggressive year-count filters don't just slow individual searches -- they create a structural shortage that takes months to notice in your funnel metrics. By the time hiring managers start asking why shortlists are thin, the root cause is requirements that were set six months ago and never revisited.
The effect on candidate experience
There's a secondary cost that rarely appears in recruiting analytics: the experience of candidates who are eliminated by year-count filters before evaluation. These are people who read your job description, believed they were qualified, applied, and were filtered out by a criterion that never actually evaluated whether they could do the job.
That experience is particularly frustrating for career changers, people early in their careers who have leveled up quickly, and candidates from non-traditional backgrounds who don't have the credential paper trail that year-count filters implicitly reward. As a TA function, the signals you send through how you filter matter to how candidates perceive your employer brand. A reputation for hard year-count requirements can quietly reduce the quality of your applicant pool over time as capable people self-select out of applying.
What to require instead
The shift is from time-based requirements to output-based ones. Instead of "5 years of backend engineering experience," the criterion becomes something like: "has independently designed and shipped at least one production service that handles real user traffic." That criterion is evaluable from a resume's project descriptions. It doesn't impose a time floor. And it captures what you actually want.
For roles where depth does correlate with time (senior leadership, specialized research), year ranges may be appropriate as one of several weighted criteria rather than a hard filter. The key word is "weighted." A candidate who doesn't meet the year threshold but shows exceptional output on other dimensions should still surface in the shortlist for human judgment.
Output-based criteria also tend to produce better intake conversations between recruiter and hiring manager. When you're negotiating "has shipped a production service with real user traffic" versus "has shipped a production service at scale serving 100K+ users," you're having a conversation about what the role actually requires. When you're negotiating "3 years versus 5 years," you're having a conversation about a proxy that neither party can directly evaluate from a resume anyway.
How to audit your own requirements before posting
A useful pre-posting step is to run your draft requirements through a brief audit: for each criterion, ask whether someone who genuinely cannot do the job could meet it with the right wording on their resume, and whether someone who clearly can do the job might not meet it because of how their career history is described. Year-count requirements typically fail both tests. A candidate who spent four years on a project that had a different title at each stage might appear to have "1 year of relevant experience" under a keyword count. A candidate who has the phrase in the right places but never did the work can appear to have more.
Running this audit on your current open requisitions, especially the ones that have been open for more than 30 days, often reveals that the thin applicant pool isn't a market supply problem but a requirements specification problem. The candidates exist; the requirements are filtering them out before they're ever evaluated. Shifting to output-based requirements and re-opening those searches frequently uncovers a larger qualified pool than anyone expected.
