Key Takeaways

  • 75% of U.S. employers use automated applicant tracking systems to screen resumes before a human reviews them (Harvard Business School & Accenture, 2021)
  • The most common ATS failures are missing keywords, incompatible formatting, and incorrect file types
  • ResumeGeni scores your resume across 8 parsing layers — modeled on the same steps enterprise ATS platforms like Workday, Greenhouse, and Taleo use to evaluate candidates

How ATS Resume Scoring Works

Applicant tracking systems parse your resume into structured data — extracting your name, contact info, work history, skills, and education — then score how well that data matches the job requirements. Many ATS rejections happen because the parser couldn't extract critical fields, not because the candidate wasn't qualified.

LayerWhat It ChecksWhy It Matters
Document extractionFile format, encoding, readabilityCorrupted or image-only PDFs fail immediately
Layout analysisTables, columns, headers, footersMulti-column layouts break field extraction
Section detectionExperience, education, skills headingsNon-standard headings cause sections to be missed
Field mappingName, email, phone, dates, titlesMissing contact info is a common cause of immediate rejection
Keyword matchingJob-specific terms, skills, certificationsKeyword overlap affects recruiter search visibility and ATS scoring
Chronology checkDate ordering, gap detectionReverse-chronological order is expected by most ATS
QuantificationMetrics, numbers, measurable outcomesQuantified achievements help human reviewers and some scoring models
Confidence scoringOverall parse quality and completenessLow-confidence parses get deprioritized in results

Frequently Asked Questions

Is ResumeGeni free?
Yes. ResumeGeni is currently in beta — ATS analysis, scoring, and initial improvement suggestions are free with no signup required. Full guidance and saved reports may require a free account.
What file formats are supported?
PDF, DOCX, DOC, TXT, RTF, ODT, and Apple Pages. PDF and DOCX are recommended for best ATS compatibility.
How is the ATS score calculated?
Your resume is processed through an 8-layer parsing pipeline that extracts structured data the same way enterprise ATS platforms do. The score reflects how completely and accurately your resume can be parsed, plus how well your content matches common ATS ranking criteria.
Can ATS read PDF resumes?
Yes, but not all PDFs are equal. Text-based PDFs parse well. Image-only PDFs (scanned documents) and PDFs with complex tables or multi-column layouts often fail ATS parsing. Our analyzer will flag these issues.
How do I improve my ATS score?
Focus on three areas: use a clean single-column format, include keywords from the job description naturally in your experience bullets, and ensure all sections (contact, experience, education, skills) use standard headings.

ATS Guides & Resources

Built by engineers with 12 years of experience building enterprise hiring technology at ZipRecruiter. Last updated .

Quantitative Research Developer - Remote

Scm · Radnor, PA

SCM is committed to a workplace that values and promotes diversity, inclusion and equal employment opportunity by ensuring that all employees are valued, heard, engaged and involved at work and have full opportunities to collaborate, contribute and grow professionally.

We’re seeking a highly driven, production-oriented quantitative research developer who has strong technical skills, first-hand experience with tick data, and interest in the intersection of market microstructure and alpha generation. SCM offers the opportunity to work in person, remotely or in a hybrid work environment. 

 

Primary Responsibilities:

  • Design, develop and support simulation frameworks for backtesting execution approaches.
  • Work with other quantitative researchers to develop new trading ideas.

 

Requirements:

  • Proficiency and experience in C++ and Python.
  • Experience researching, building and maintaining trading systems utilizing market data.
  • Strong understanding of data path from tick to trade.
  • Experience analyzing time series data.
  • Experience with large data sets.
  • Excellent verbal and written communication skills.
  • Strong work ethic and desire for excellence.
  • Desire to think critically and creatively.

 

The base pay for this position is anticipated to be between $150,000 and $300,000 per year. The anticipated annual base pay range is current as of the time this job post was generated. This position is eligible for other forms of compensation and benefits, such as a bonus, health and dental plans and 401(k) contributions, which includes a discretionary profit sharing program. An employee's bonus and related compensation benefits can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.