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 .

Data Analyst

Cube · London

CUBE are a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. We deliver our services through intuitive SaaS solutions, powered by AI, to simplify the complex and everchanging world of compliance for our clients. 

 

Why us?

🌍 CUBE is a globally recognized brand at the forefront of Regulatory Technology. Our industry-leading SaaS solutions are trusted by the world’s top financial institutions globally.

🚀 In 2024, we achieved over 50% growth, both organically and through two strategic acquisitions. We’re a fast-paced, high-performing team that thrives on pushing boundaries—continuously evolving our products, services, and operations. At CUBE, we don’t just keep up we stay ahead.

🌱 We believe our future is built by bold, ambitious individuals who are driven to make a real difference. Our “make it happen” culture empowers you to take ownership of your career and accelerate your personal and professional development from day one.

🌐 With over 700 CUBERs across 19 countries spanning EMEA, the Americas, and APAC, we operate as one team with a shared mission to transform regulatory compliance. Diversity, collaboration, and purpose are the heartbeat of our success.

💡 We were among the first to harness the power of AI in regulatory intelligence, and we continue to lead with our cutting-edge technology. At CUBE, You will work alongside some of the brightest minds in AI research and engineering in developing impactful solutions that are reshaping the world of regulatory compliance.

Role Mission:

The Data Analyst role within the Customer Data Operations team is pivotal in driving the value and effectiveness of the CUBE Platform. This role involves leveraging advanced analytical skills to connect and optimise customer risk and control data within the CUBE Network. The position also focuses on improving data processes and ensuring that CUBE’s Network data reflects a consensus view of customers' risk and control data with the highest standards of quality and consistency.

Key Responsibilities

· Support Subject Matter Experts (SMEs) in the process of connecting customer data to the CUBE Network using advanced data matching protocols, tools, and Python-based solutions.

· Collaborate with subject matter experts to deliver high-quality, accurate data insights for customers while continuously improving your own expertise in risk and control frameworks.

· Work closely with the Product and Engineering teams to design and implement improved data processing workflows and tools, leveraging Python for automation and analysis.

· Assist in defining and refining CUBE’s consensus-based risk and control indices and business rules to maintain Network consistency.

· Ensure the ongoing quality, consistency, and accuracy of CUBE’s data, applying technical standards and quality control rigorously across all core content.

· In addition to the duties and responsibilities listed above, the postholder may also be reasonably expected to perform duties of a similar or related nature to those outlined in the job description, and/or to undertake ad hoc or special responsibilities, in line with all other employees of the company. However, such tasks or projects will not be expected to form a dominant part of the individual’s role.

Skills & Competencies

· Data Management & Quality: Executes data cleansing, validation, and enhancement tasks. Ensure data accuracy and consistency under supervision.

· Risk & Governance: Understands basic data governance and control principles. Supports compliance and data integrity checks.

· Tools, Automation & AI: Demonstrates proficiency in Python, SQL, Excel, and visualisation tools. Begins exploring AI and ML automation techniques.

· Problem-Solving & Innovation: Applies structured problem-solving to resolve data issues and suggest small process enhancements.

· Communication & Collaboration: Communicates clearly with team members and contributes accurate data outputs to shared projects.

· Operational Execution: Completes assigned data tasks efficiently and on time, following defined workflows.

· Mentoring & Leadership: Seeks guidance from peers and shares knowledge with the team. Build confidence through learning and feedback.

Required Experience & Qualifications

· Some experience in a data-focused role.

· Proficiency in MS Office, particularly Excel.

· Familiarity with working in a fast-paced, scale-up environment, preferably within the FinTech or RegTech sectors.

· Self-starter with a pragmatic and proactive approach.

· A collaborative and flexible team player who can contribute positively to the company’s culture.

· Proven self-starter with a pragmatic and proactive approach to problem-solving and innovation.

· Proficiency in at least one of the following data analysis tools and languages, such as Python, SQL, MS/Azure Foundry and data visualisation tools (e.g., Tableau, Power BI).

Performance Indicators

· Timely dentification of quality issues within datasets.

· Timely and successful completion of data processing tasks.

· Contribution to improving data processing efficiency in collaboration with Product, Data Science, and Engineering teams.

· Positive feedback from senior analysts, subject matter experts, and other stakeholders regarding data quality and insights.

· Adherence to CUBE’s technical standards and business rules in all deliverables.

· Demonstrated growth in understanding risk and control frameworks and application of this knowledge in day-to-day work.

Interested?

If you are passionate about leveraging technology to transform regulatory compliance and meet the qualifications outlined above, we invite you to apply. Please submit your resume detailing your relevant experience and interest in CUBE.​

CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.