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 .

Lead/Senior Quantitative Analyst, Predictive Modeling

Philliesbaseballoperations · Philadelphia

Title: Lead/Senior Quantitative Analyst, Predictive Modeling
Department: Baseball Research & Development
Reports to: Director, Predictive Modeling
Status: Regular Full-Time
Location: Philadelphia, PA; also open to Remote

Job Description
As a Lead/Senior Quantitative Analyst, Predictive Modeling, you help shape the future of Phillies Baseball Operations by building statistical models to forecast player performance and communicating those results to decision-makers. Using analytical rigor and sophisticated statistical modeling techniques, you identify opportunities for the Phillies to improve via the application of forecasts to player development and evaluation. Join a team doing cutting-edge foundational research on biomechanics, human movement, ball-flight physics, and more, with the unique opportunity to apply those findings to player evaluation.

Responsibilities

  • Conduct and oversee statistical forecasting projects in multiple baseball subject areas
  • Collaborate with baseball subject matter experts in scouting, development, biomechanics, machine learning, decision science, and more, integrating their expertise into player evaluation models
  • Maximize organizational impact of the department’s player evaluation models by advocating model-driven decision-making in various baseball contexts
  • Ensure projects conform to best practices for implementing, maintaining, and improving predictive models throughout their life cycles
  • Assist and mentor other members of the QA team with their projects by providing guidance and feedback on your areas of expertise within baseball and statistical modeling
  • Continually enhance your and your colleagues’ knowledge of baseball and data science through documentation, reading, research, and discussion with your teammates and the rest of the front office

Required Qualifications

  • 2-5+ years of relevant work or graduate school experience
  • Possess or are pursuing a BS, MS or PhD in Statistics or related (e.g., mathematics, physics, or ops research) or equivalent practical experience
    ○ To determine leveling we look at a variety of factors including, but not limited to,
    years of experience and education. Typically we consider candidates as Lead QA
    around 2-3 years of experience and Senior QA around 4-5+ years of experience
  • Proficiency with scripting languages such as Python, statistical software (R, S-Plus, SAS, or similar), and databases (SQL)
  • Demonstrated experience designing, constructing, implementing, and leading technical research projects for use by non-technical stakeholders
  • Proven willingness to both teach others and learn new techniques
  • Willingness to work as part of a team on complex projects
  • Proven leadership and self-direction

Preferred Qualifications

  • Experience with a probabilistic programming language (Stan, PyMC, etc.)
  • Experience managing or overseeing the work of other data scientists or analysts
  • Experience with model-driven decision-making under uncertainty (eg. a rigorous approach to fantasy sports, poker, etc.)

Interested applicants should submit both their resume and an answer to the following question:

The R&D department has been asked to identify the best defensive center fielder in baseball. What models would you build to answer that question, and how would you apply those models to decision-making? (250 word limit)

Tip: There’s no defined right or wrong answer. Responses are used to get some insight into how you approach problem solving and baseball in general.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.

 

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