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 .

Senior Data Scientist II

Careem · Dubai, United Arab Emirates

Careem is building the Everything App for the greater Middle East — making it easy to move around, order food and groceries, manage payments, and more. Our purpose is simple: to simplify and improve people’s lives and build an awesome organisation that inspires.
Since 2012, Careem has enabled earnings for over 2.5 million Captains, simplified the lives of more than 70 million customers, and built a platform where the region’s best talent and entrepreneurs thrive. We operate in 70+ cities across 10 countries, from Morocco to Pakistan.

We’re now entering our next chapter — one powered by AI. We’re looking for AI talent: curious problem-solvers who know how to apply AI to build tools, automate workflows, and create real impact. Whether it’s streamlining operations, enhancing customer experience, or reimagining internal systems — we want people who can make Careem work smarter and move faster.

About the team

The Careem Data Science team’s mission is to drive competitive value from data at scale through building AI models to optimize user experiences, decision-making, operational efficiencies, and lead the region’s AI ecosystem. As one of the tech leaders in this team, you will be at the forefront of fulfilling this mission. You will be working with the top data science talent of the region, while innovating on our user experience using GenAI.

What you'll do

  • Be part of a 0-1 AI transformation for the Careem app from a personalization perspective.
  • Build a long-term vision on how we can rethink our customer acquisition and engagement strategies, leveraging data in our decision-making.
  • Drive exploratory analysis to understand the ecosystem, user behavior, and identify new levers to help move metrics and build models of user behaviors for analysis and product enhancements.
  • Shape and influence data/ML models and instrumentation to optimize the product experience and generate insights on new areas of opportunity and new products.
  • Provide product leadership by sharing data-based recommendations to communicate the state of the business, the root cause of change in metrics, and experimentation results, influencing product and business decisions
  • Implement scalable machine learning algorithms that will be used in production on big data.
  • Embark on exploratory data analysis projects to achieve a better understanding of phenomena as well as to discover untapped areas of growth and optimization. 
  • Answer complex analytic questions from big data sets to help Careem shape its products and services in a better way.
  • Help define and track the appropriate key metrics for specific projects. 
  • Design and run randomized controlled experiments, analyze the resulting data, and communicate results with other teams.
  • You will always challenge the status quo and continually investigate new data processing technologies and seek to ensure that we follow the industry best practices.
  • Build and deploy retrieval augmented generation systems and other applications of large language models.

What you'll need

  • 7-9 years of experience in data mining, predictive modeling, time series analysis, machine learning, Big Data methodologies, and transformation and cleaning of both structured and unstructured data.
  • Advanced degree in a quantitative discipline such as Physics, Statistics, Mathematics, Engineering or Computer Science.
  • Solid experience with Deep Learning Techniques (e.g, attention, retrieval models, etc)
  • 1-2 years of industrial experience in personalization, recommendation or search is a MUST. Preferably working in a product-driven company.
  • Strong problem-solving and coding skills.
  • Solid knowledge of AB testing, classic ML, and DL
  • Solid understanding of recommendations, ranking, and retrieval. 
  • Proficiency and demonstrated experience in: Python, SQL, Spark, Hive.
  • Demonstrated experience with database technologies (e.g. , Hadoop, BigQuery, Amazon EMR, Hive, Oracle, SAP, DB2, Teradata, MS SQL Server, MySQL).
  • Demonstrated experience with business intelligence and visualization tools (Tableau, MicroStrategy, ChartIO, Qlik) along with geospatial data processing skill, is a plus.
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