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 .

Credit Risk Manager

Abound · Madrid

About Abound

We’re redefining consumer lending in the UK, and beyond. Using advanced AI and Open Banking data, we make fair, affordable personal finance available to more people. While traditional lenders rely almost entirely on credit scores, we look at the full financial picture - how much you spend, and what you can afford to repay to build a deeper, more accurate understanding of each customer's unique financial situation.


And we've shown it works at scale. We’ve issued over £1.3bn in loans directly to customers while delivering market-leading credit performance - for every 10 defaults the industry expects, we see only 3. We also reached profitability just 2.5 years after launch.


Backed by £2bn+ of funding from top-tier investors including Citi, GSR Ventures, and Deutsche Bank, we’re recognised as one of Europe’s fastest-growing fintechs (Sifted, CNBC). Now, we’re expanding into new markets and product lines - and we’re looking for ambitious people who want to learn fast, take ownership, and grow with us.

About the role:

We’re hiring a Credit Risk Manager/Data Scientist to own credit risk and data science in Spain. We know that making our product work in Spain will involve adapting it. You’ll need to find and integrate new data sources, build models and features, and measure performance to give us the foundation to grow in the Spanish market.

What you'll be doing:

  • Owning credit performance, data science and reporting in Spain end to end

  • Building and owning our local decisioning model and decisioning features

  • Owning our API integrations, and relationships, with all local data providers

  • Working closely with our local product, tech, underwriting and operations teams to constantly tweak and develop new features

  • Appreciation of the trade-off between risk and growth, and the levers we have to drive both

  • Understanding, evaluating and localizing what’s worked in the UK and adapting what makes sense to the local market

  • Overseeing the monitoring and reporting of key credit performance triggers and concentration metrics

Who You Are:

  • A rigorous, disciplined, and hands-on professional with a strong sense of ownership and accountability.

  • Comfortable operating in ambiguity. We don’t have the answers to what will work in Spain, but you’ll work tirelessly to find them

  • Technically fluent in credit risk concepts, portfolio metrics, concentration analysis, and performance monitoring.

  • Confident challenging data, assumptions, and narratives in a constructive, commercially grounded way.

  • Highly organised, with strong execution and stakeholder management skills.

  • Credible and composed when engaging with senior management and external counterparties.

Experience & Background:

  • Around 5 years’ experience in a numerate, analytical, or risk-focused role (e.g. credit risk, finance, consulting, audit, analytics, operations) in Spain

  • Experience across one or more consumer lending products such as Unsecured Personal Loans (UPL), BNPL, Mortgages, or Auto finance.

  • Experience producing, reviewing, or owning MI in a multi-product or multi-channel environment.

  • Familiarity with credit risk performance metrics, portfolio monitoring, concentration analysis, and performance triggers.

  • Experience working with internal risk limits and/or external covenants, triggers, or funding-related reporting.

  • Strong written and verbal communication skills, with the ability to present complex topics clearly to senior audiences.

  • Does not need to be a deep data scientist, but must be comfortable working closely with data scientists and getting into the detail where required.

Why Abound:

  • Take ownership of a critical role supporting Abound’s continued growth and expansion into new products and markets.

  • Work closely with the local Spanish team, CRO, senior leadership, product teams, and external stakeholders.

  • Help shape scalable, best-in-class credit and product risk reporting and governance.

  • Operate in a high-trust role with real responsibility, visibility, and impact.