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, Fraud Prevention

Gocardless · London, UK

About Us at GoCardless

GoCardless is a global bank payment company. Over 100,000 businesses, from start-ups to household names, use GoCardless to collect and send payments through direct debit, real-time payments and open banking. 

GoCardless processes US$130bn+ of payments annually, across 30+ countries; helping customers collect and send both recurring and one-off payments, without the chasing, stress or expensive fees. We use AI-powered solutions to improve payment success and reduce fraud. And, with open banking connectivity to over 2,500 banks, we help our customers make faster, more informed decisions.

We are headquartered in the UK with offices in London and Leeds, and additional locations in Australia, France, Ireland, Latvia, Portugal and the United States.

At GoCardless, we're all about supporting you! We’re committed to making our hiring process inclusive and accessible. If you need extra support or adjustments, reach out to your Talent Partner — we’re here to help! 

And remember: we don’t expect you to meet every single requirement. If you’re excited by this role, we encourage you to apply!

The role

This role will be working within the Fraud Prevention team in our Merchant Operations Group. The Fraud Prevention team plays a critical role in protecting the integrity of the GoCardless platform by building systems that prevent and detect merchant fraud before it impacts our business or our customers. 

The Fraud Prevention Data Scientist will work closely with Engineers and Fraud Analysts to develop and deploy predictive models that strengthen our fraud defenses. You’ll focus on the end-to-end delivery of ML solutions - from feature engineering and prototyping to production-grade deployment - to reduce false positives and automate controls without introducing unnecessary friction. You’ll also collaborate with cross-functional stakeholders to ensure our ML products scale on our GCP stack, driving fintech innovation while supporting a seamless customer experience.

What you’ll do

  • Contribute to the end-to-end delivery of models at scale, from initial discovery and feature engineering to production, A/B testing and continuous monitoring.
  • Collaborate with product, engineering and data science peers to turn complex data into real-time, mission-critical fraud prevention solutions.
  • Raise the team’s collective bar through hands-on technical leadership and knowledge sharing.
  • Help bring to live the latest developments in ML and payer fraud prevention to drive innovation at GoCardless.

What excites you

  • Being a self-starter who thrives on taking a vague business problem and owning the journey from the first prototype to a live, measurable solution.
  • Contributing to the future of fraud prevention, by shaping up the data and ML products all the way from the initial insights to the market-ready solutions.
  • Working with a range of stakeholders to discover and design ML solutions, adapting them to the markets as we grow.
  • Building production-grade ML models on a streamlined GCP and Vertex AI stack to drive fintech innovation.

What excites us

  • You hold a degree (or PhD) in a STEM discipline or an equivalent commercial experience.
  • You have a track record of deploying predictive models and data products in production with quantifiable impact (experience in Fintech, Fraud Prevention, or Payments is a big plus).
  • You can translate complex ML concepts into practical product solutions and communicate these ideas clearly to non-technical peers.
  • You are experienced with writing and maintaining code to a production-level standard, supporting the team with code reviews.
  • You are comfortable contributing across the full model lifecycle, from deep-dive analysis and feature engineering to prototyping, validation, and live A/B testing.

 

Base salary range: £99,200 - £148,800

 

Base salary ranges are based on role, job level, location, and market data.  Please note that whilst we strive to offer competitive compensation, our approach is to pay between the minimum and the mid-point of the pay range until performance can be assessed in role. Offers will take into account level of experience, interview assessment, budgets and parity between you and fellow employees at GoCardless doing similar work.

The Good Stuff!

  • Wellbeing: Dedicated support and medical cover to keep you healthy.
  • Work Away Scheme: Work from anywhere for up to 90 days in any 12-month period.
  • Hybrid Working: Our hybrid model offers flexibility, with in-office days determined by your team.
  • Equity: All permanently employed GeeCees get equity to share in our success.
  • Parental leave: Tailored leave to support your life's great adventure.
  • Time off: Annual holiday leave based on your location, supplemented by 3 volunteer days and 4 wellness days.

Life at GoCardless

We're an organisation defined by our values; We start with why before we begin any project, to ensure it’s aligned with our mission. We make it happen, working with urgency and taking personal accountability for getting things done. We act with integrity, always. We care deeply about what we do and we know it's essential that we be humble whilst we do it. Our Values form part of the GoCardless DNA, and are used to not only help us nurture and develop our culture, but to deliver impactful work that will help us to achieve our vision.

Diversity & Inclusion

We’re building the payment network of the future, and to achieve our goal, we need a diverse team with a range of perspectives and experiences. As of July 2024, here’s where we stand:

  • 45% identify as women 
  • 23% identify as Black, Asian, Mixed, or Other 
  • 10% identify as LGBTQIA+ 
  • 9% identify as neurodiverse 
  • 2% identify as disabled 

If you want to learn more, you can read about our Employee Resource Groups and objectives here as well as our latest D&I Report 

Sustainability at GoCardless

We’re committed to reducing our environmental impact and leaving a sustainable world for future generations. As co-founders of the Tech Zero coalition, we’re working towards a climate-positive future. Check out our sustainability action plan here. 

Find out more about Life at GoCardless via X, Instagram and LinkedIn