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 Scientist - Mumbai

Modulr Finance · Mumbai

•Are you curious, excited by experimentation and always looking to innovate?
•Do you want to work in embedded payments where you can keep learning and developing whilst getting hands-on experience?
•Do you want to have the opportunity to play an important role in a rapidly growing and exciting Fintech business?
If so, we would love to connect and collaborate!

About us
At Modulr, our vision is a world where all businesses are powered by embedded payments. Modulr enables businesses, from SMEs to Enterprise, initially across the UK and Europe to efficiently pay-in, collect and disburse funds instantly via a range of payment schemes, accounts, and card products.
We have created an industry-leading API platform with comprehensive online tools and access, to meet the demands of daily business payments. We have two routes to market. Our Core Business Payments product allows customers in any sector to connect to us and our expanding network of accounting and payroll platforms, including Sage, Xero, BrightPay and IRIS to automate payments. Our Vertical Solutions targets a growing range of industry verticals which directly connect their IT platforms to our APIs and webhooks. We solve complex payment problems for hundreds of clients in a range of industries, including Travel, Lending, Wage Advance, and Investment & Wealth.
We are deeply integrated into the payment eco-system. In the UK, we are direct participants of Faster Payments and Bacs. Modulr hold settlement accounts at the Bank of England. Our payment network connectivity includes CHAPS, Open Banking, SEPA, SWIFT and account issuance in multiple currencies. We are principal issuing members of Visa and Mastercard schemes across UK and Europe. Our regulatory permissions and governance structure are the foundations of our business. We are regulated and supervised as an Authorised Electronic Money Institution (AEMI) in the UK by the Financial Conduct Authority and in the Netherlands by De Nederlandsche Bank.
Our founding team has a wealth of experience in the payments industry and growing successful businesses. Modulr is backed by the venture arms of payments giants PayPal and FIS, as well as growth investors Blenheim Chalcot, General Atlantic, Frog Capital and Highland Europe.
Modulr now has over 400 employees spread globally across offices in London, Edinburgh, Amsterdam, and Mumbai.

Modulr values
•Building the extraordinary; going that extra mile.
•Owning the opportunity; be passionate and proud of the time you invest.
•Move at pace; reach goals faster whilst supported on your career journey.
•Achieve it together, working collaboratively and being a Modulite.

About the Role:
We are looking for a passionate Data Scientist with 2–3 years of experience to build ML/DL and Generative AI solutions for high-impact financial use cases.

Key Responsibilities:
• Develop ML/DL models for fraud detection, risk scoring, and personalization
• Build and deploy GenAI applications using LLMs
• Design and implement Agentic AI workflows
• Work with LangChain and LangGraph for orchestration
• Use tools like Claude, Cursor/CoPilot for AI-assisted development
• Implement monitoring using LangSmith
• Build APIs for model deployment
• Collaborate with cross-functional teams
• Maintain MLOps pipelines and CI/CD workflows

Key Requirements:
• 2–3 years experience in Data Science / ML / DL
• Strong Python skills
• Experience with ML frameworks (PyTorch, TensorFlow, XGBoost)
• Hands-on with GenAI, LLMs, prompt engineering
• Experience with LangChain, LangGraph
• Familiarity with Agentic AI concepts
• Exposure to tools like Claude, Cursor/CoPilot
• Experience with LangSmith for debugging/monitoring
• Knowledge of APIs, FastAPI/Flask, Docker
• Experience with MLOps tools (MLflow, DVC)
• FinTech domain experience preferred

ModInclusion 

We believe that by seeing Modulr, and the world, from all sorts of angles, we can make life better for all.​ We want you to know that the things that make you, you — like your identity, age, ability, and background — are things that we will always celebrate and support with open arms.  As such, we are keen to maximise the diversity of our workforce and actively encourage applications from anyone and everyone.