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 .

Analytics Engineer

Gaiafamily · London

Location: London or South America

Experience: 3+ years experience in a similar role.

The role:

Gaia is building the category-defining family building company. We are looking for a company-wide Data Analyst to serve as the analytical spine of the company, turning data into insights to better inform decisions across Product, Growth, Operations, and Finance.

This is a high-impact, hands-on role. You will own Gaia’s source of truth, proactively surface insights, and raise the bar on how the company uses data to operate, prioritize, and scale.

What will you own?

  1. Company-wide analytics and source of truth data for our BI tool

    1. Responsible for the delivery and usage of the data pipeline, including how the data is modelled

    2. Own and maintain core dashboards and metrics

    3. Ensure data is accurate, trusted and decision ready through daily monitoring - this includes data backfill when needed

    4. Define and evolve the metrics used to run Gaia

    5. Surface leading indicators

  2. Partner with product and engineering to ensure data requirements are taken into account before development work happens, defining the right questions, surfacing insights and supporting decision making

  3. Modelling, monitoring, experimenting and forecasting

Who are we looking for?

  • Former Growth, Ops, or BizOps analysts who want to move closer to real decisions and accountability

  • Analysts who have sat in high-velocity teams to understand funnels, unit economics, trade-offs, and constraints.

  • Comfortable with our current data stack of Python, SQL, dbt, Prefect, Omni (or a similar BI tool) and Fivetran

  • You are data curious, and won’t hesitate to challenge business decision with relevant insights

  • You know how to balance speed and thoroughness - you like going down data rabbit holes to deeply understand a problem but also know when to make do with available data to make quicker decisions when needed

Technical competencies Essential

  • SQL

  • Familiarity with any modern bi tool: Omni, Looker, Metabase, Tableau etc

  • Excel

  • Python

Nice to haves

  • dbt

  • data warehouse experience: snowflake, big query, redshift, databricks etc

  • Use of data extractions tools: fivetran, air byte, stitch etc

  • Use of data orchestration tools: Airflow, Prefect, Dagster etc

  • Familiar with hubspot

What success looks like

  • High data richness, quality and accuracy

  • Consistent metrics used to make decisions

  • Faster, higher-quality decisions across Product, Growth, Finance and Operations

  • Clear visibility into funnel health, unit economics, and operational leverage

  • Fewer surprises, earlier course-correction

How do we work: