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 .

Quantitative Researcher (Full-Time - PhD+)

Radixuniversity · Chicago, New York, Amsterdam

Please only apply to one of our Job Postings. At the bottom of the application questions below you'll have the option to indicate if there are any other roles here at Radix that you might be interested in. Please do not submit multiple applications for different positions.  

As a Quantitative Researcher, your focus is on identifying trading opportunities, but you can add even more value with strong quantitative skills and some coding proficiency to accelerate the innovation process and help others leverage your work. 
 
By working on a variety of projects with different collaborators over the start of your career, you’ll gain new knowledge and insight into the fundamentals of market dynamics, trading strategies, and our proprietary research platform. We believe in learning through impactful work, so while you learn the intricacies of our industry, you’ll have plenty of opportunities to contribute and directly affect our bottom line within your first few weeks on the team.  
 
While interest in trading is key, a background in finance is definitely not. Our team is built mostly from academia — not from other trading firms. We seek mental diversity and add a select group of academics each year from a wide range of disciplines. 
 
COMPENSATION – Competitive salary, plus quarterly bonus based on individual performance and contribution towards success of others and the firm.
 
Qualifications
 
We’re looking for highly analytical people (math, physics, computer science, statistics, electrical engineering, etc.) who want to help build the research-driven trading firm of the future. To do that, you’ll need the following qualities:

  • Currently a PhD Student, Postdoc, Professor, or hold a similar advanced research position at a University
  • Persistent Drive to Improve - Do you have an innate desire to rise to the next level, even after great accomplishment?
  • Creative Problem Solving and Probabilistic Thinking - You must enjoy learning and implementing new concepts quickly, combining knowledge from different domains to create new ideas, and take a data-driven and probabilistic approach to testing and implementing new ideas.
  • Team Mindset - We want people who understand 1+1 > 2 and are as committed to making the team better through sharing ideas as they are driven to improve their individual performance.
  • Mental Flexibility & Self Awareness - You’ll have to frequently adapt based on new data, results, and feedback on your trading ideas and your performance.
  • Orientation for Making Money - Although we value academic training, our work is not an academic exercise. We take a hacker’s approach to testing ideas, dropping projects that consume time without high upside, and focusing our next efforts on what will create the most value for the firm.

Research / Quant trading strategy skills to have or develop

  • Strong intuition and deep thinking with data sets - Designs new alphas, understands complex systems; knows where to start, or ask others where to start
  • Demonstrates strong “hacking” ability to quickly get into data to look for empirical relationships and decipher noise or signal
  • Familiarity with classical statistical methods and knows when and how to apply them in a rigorous fashion; Easily learns how to apply new statistical methods; will seek out and learn new methods to better solve problem
    • Experience with modern AI techniques and methods or desire to work on Applied Machine Learning Problems a plus
  • Constantly questions finance/trading data and stays motivated to seek answers despite most often proving that there is no correlation or signal
  • Experience in setup of research framework and execution of projects
  • Understanding of financial products, market dynamics, and microstructure
  • Experience programming in Low-level computer languages (like C++); awareness of strength in particular language and ability to solve more complex problems due to understanding nuances of the language