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 .

Staff Data Scientist

Verve · Bangalore, Karnātaka, India

Who We Are

Verve has created a more efficient and privacy-focused way to buy and monetize advertising. Verve is an ecosystem of demand and supply technologies fusing data, media, and technology together to deliver results and growth to both advertisers and publishers–no matter the screen or location, no matter who, what, or where a customer is. With 30 offices across the globe and with an eye on servicing forward-thinking advertising customers, Verve’s solutions are trusted by more than 90 of the United States’ top 100 advertisers, 4,000 publishers globally, and the world’s top demand-side platforms. Learn more at www.verve.com.

About the Role

In this role you will work closely with product, engineering and other teams, collaborate with other Data Science team and with the Machine Learning Engineers to engineer prototypes into solutions.

Domain

In this role your main focus would be on our Audience generation and insight projects

  • Audience generation: ML for embedded targets, audience privacy first approaches, Composite AI agents, etc..

  • Audience insight - describe audience composition and characteristics

  • Adhoc analysis - support business request to better understand our data assets when data is not readily available from our BI tools

  • Support sales pitch - provided valuable extra insight to augment the value proposition for key accounts


What You Will Do

Research and Development

Our Data Science role includes the following responsibilities:

  • Research and development of cutting edge Machine Learning systems, models, and schemes in many different areas of Adtech

  • Develop real-time algorithms for audience creation and segmentation

  • Discover insights/patterns in our customers from various data sources such as exchange data, behavioural data, location data, 1 and 3rd party data assets

  • Design experiments, oversee A/B testing, evaluate the quality of derived assets and continuously monitor model performance

  • Create proof of concepts and data science prototypes

  • Search and select appropriate data sets

  • Perform statistical analysis and use results to improve models

  • Identify differences in data distribution that could affect model performance in real-world situations

  • Visualize data for deeper insights

  • Analyze the use cases of ML algorithms and ranking them by their success probability

  • Understanding when your findings can be applied to business decisions

  • Reducing business problems into Machine Learning problems and opportunities

  • Verifying data quality

Collaborative Work

  • Work closely with product, engineering, and sales teams to drive the use of Data Science across the Verve Group

  • Collaborate with our Exchange Data Science team

  • Collaborate with Machine Learning Engineers to engineer prototypes into solutions

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