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 .

Manager 2, AI Science Mountain View, California

Intuit

Intuit is seeking a Manager 2, AI Science to join Intuit AI team.

 

Come join our collaborative and creative group of AI scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches. We are waiting for you to join us and do the best work of your life.


Responsibilities

  • Lead and create a team of AI scientists
  • Mentor and hire the best AI scientists in the valley
  • Apply artificial intelligence and machine learning techniques to solve complex questions or fuel new business opportunities
  • Deliver breakthrough benefits to Intuit users/customers across small business & consumer products using individual, enriched, and aggregated data
  • Provide leadership in advanced engineering, AI science and analytics in the development of current or future products or technologies
  • Provide technical leadership across multiple teams, by understanding a key technology space deeply enough to help guide strategy
  • Provide/inspire AI science innovations that fuel the growth of Intuit as a whole
  • Understand and teach proven methods and hacking skills in working with divergent data types at scale, to explore and extrapolate data-driven insights using advanced, predictive statistical modeling and testing applied to data acquired and cleansed from a range of sources
  • Provide to business stakeholders the entrepreneurial guidance essential for appropriately interpreting and building on findings, and fully exploiting the insights revealed through the research

Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing pay equity for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.


Qualifications

  • BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Econometrics, Operations Research, Physics, etc.)
  • 2+ plus years experience as an established technical leader/Manager of AI science teams that have successfully delivered data-driven software products.
  • Innovative and disruptive technology leader solving long term strategic outcomes
  • Expert command of AI and machine learning, statistical modeling, state-of-the-art tools, and engineering best practices
  • Experience in leading teams who have expertise in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, genetic algorithms, anomaly detection, recommender systems, and natural language processing.
  • Impeccable attention to detail and strong ability to convert complex data into insights and action plans
  • Strategic thinker, flexible problem solver, great listener and team orientation
  • Able to effectively communicate a vision and inspire others to innovate
  • You thrive on ambiguity and enjoy the frequent pivoting that’s part of the exploration.  You are extremely passionate about applying practically Machine Learning to real customer problems, powering the next generation experiences for large scale applications.

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: 

Bay Area California,CA: $231,000 - $312,500. 


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