Key Finding: Most Resumes Fail ATS Screening
Our analysis of 10,247 resumes scanned through the CareerOS ATS Checker between January and June 2026 reveals a striking reality: 77% of resumes scored below 80, the threshold where recruiters are likely to review them. The average score was just 52 out of 100.
This means the majority of job applicants are submitting resumes that automated systems effectively bury. The good news? Most of these issues are fixable with targeted changes.
Top 5 Reasons Resumes Score Poorly
Missing Keywords
67% affectedResumes lacked keywords present in the job description. The average resume matched only 41% of relevant keywords.
Formatting Issues
43% affectedTables, columns, headers/footers, and graphics confused ATS parsers and scrambled content.
Non-Standard Headings
38% affectedCreative section names like "My Journey" or "What I Bring" were not recognized by ATS as valid sections.
No Quantified Results
52% affectedBullet points without metrics (numbers, percentages, dollar amounts) scored lower on experience relevance.
Wrong File Format
19% affectedSubmitted as .pages, .rtf, or image-based PDFs that ATS systems could not parse.
File Format Performance
File format matters more than most candidates realize. Here's how each format performed on average:
What Top-Scoring Resumes Have in Common
The 23% of resumes that scored above 80 shared these characteristics:
- ✓Matched 70%+ of keywords from the job description
- ✓Used standard section headings (Work Experience, Education, Skills)
- ✓Included quantified achievements in 80%+ of bullet points
- ✓Single-column layout with no tables or columns
- ✓Saved as .docx or simple single-column PDF
- ✓Contact information in the body, not in headers/footers
- ✓Tailored summary paragraph for the specific role
Methodology
This study analyzed 10,247 anonymized resume scans performed through the CareerOS ATS Checker tool between January 1 and June 30, 2026. Each resume was scored against the candidate's target job description using our ATS scoring algorithm, which evaluates keyword matching, formatting quality, experience relevance, and education alignment. All personally identifiable information was stripped before analysis. Data was aggregated by industry, file format, and scoring category.