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 .

Financial Data Research Analyst

Uber · Mumbai

Full-time Professional Data Experienced Candidates Data Core Mumbai


Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors. 
 
Your future role within QRT 

In this role, you will serve as a key contributor to our financial data research efforts. You will handle extraction and processing of data from various financial sources like websites, PDFS, and other ad hoc sources. Your work will play a crucial role in ensuring that traders, quant researchers, and technology teams have access to accurate and timely data, facilitating informed decision-making across the organization. 

Key Responsibilities: 

  • Conduct in-depth research analysis of financial data from multiple sources, including Bloomberg, Thomson Reuters Eikon, S&P Capital IQ, FactSet, and others,

  • Define robust processes to manually collect data from complex documents along with PDFs, images, and audio files, requiring a solid understanding of the subject matter documents and other relevant platforms. 

  • Actively track news and updates across various sectors and share key insights on a daily and weekly basis, including relevant links.
  • Leverage strong experience in Excel macros and Python coding to further enhance data handling and automation processes. 
  • Collaborate closely with cross-functional teams to understand data needs and ensure that the right data is provided in a user-friendly format. 
  • Monitor data accuracy and resolve any discrepancies or issues that may arise. 
  • Provide support and guidance to users on utilizing the data effectively for their respective needs. 
  • Stay updated with the latest trends and tools in financial data research, automation, and alternative data sources. 
  • Exposure to and potential involvement in projects related to Large Language Models (LLMs) or ChatGPT, enhancing the data interaction experience. 

Your Present Skill Set: 

  • Bachelor’s degree in finance, Computer Science, Data Science, or a related field. 
  • 6+ years of experience in financial data research and analysis, with a strong understanding of financial markets and instruments. 

  • Good with Python scripts to automate the extraction, processing, and validation of financial data from various sources.
  • Experience with data sources such as SEC EDGAR, earnings calls, and alternative data platforms like Quandl. 
  • Excellent analytical skills, with attention to detail and a strong focus on accuracy. 
  • Ability to work collaboratively with traders, quant researchers, and technology teams. 
  • Effective communication skills, both written and verbal, with the ability to present complex data in a clear and concise manner.

  • Exposure to Large Language Models (LLMs) or ChatGPT is a plus.