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, Credit Risk

Koho · KOHO (CAN)

About KOHO

We’re on a mission to make financial services better for every Canadian. That means no hidden fees, no predatory interest rates - just financial products designed to help our users spend smart, save more, and build real wealth. We’re a performance organization with a strong heart: we care deeply about outcomes, and everything ties back to our mission - to financially empower a generation of Canadians.

At KOHO, we’re not your average 9-5. We believe real impact comes from people who are trusted, empowered, and supported to do their best work - without sacrificing their lives to do it. We prioritize work-life integration, not just work-life balance. That means asynchronous collaboration, flexible hours, and a remote-first setup built around autonomy and high trust.

KOHO is entering its next chapter - leaner, smarter, more AI-integrated. We’re building for impact, not bureaucracy. If you thrive in environments that value clarity, ownership, and bold thinking, you’ll fit right in.

About the Role

We're building a world-class financial product and we need someone to help take our data operations to the next level. Our team is growing fast, and we’re looking for a Predictive Modeller to join us. You understand the data-driven decision making needs of a high-growth organization and are focused on concrete outcomes and KPIs. You look for the highest leverage solution to the most important problems, through either pragmatic analysis, a predictive model, or unsupervised learning methods.

What you'll do

  • Design and develop statistical and machine learning models for credit risk parameters (PD, EAD, LGD) across lending products including credit card, line of credit, overdraft, BNPL, etc.

  • Execute full model development lifecycle from data exploration and feature engineering through validation and deployment

  • Implement advanced modelling techniques including regression, classification, ensemble methods, and deep learning algorithms

  • Conduct model performance monitoring, champion-challenger testing, and regulatory compliance validation

  • Collaborate with Risk Management, Credit, and Product teams to translate business requirements into technical specifications

  • Create automated dashboards, reports, and ad-hoc analyses to support strategic business decision-making

  • Document model methodology, results, and insights

  • Lead model refresh initiatives and back-testing procedures to maintain predictive accuracy and performance

Who you are:

  • 5+ years of experience in predictive modelling with demonstrated collaboration across data science, engineering, and product teams

  • Proven experience developing credit risk models (PD, EAD, LGD) for consumer lending products including credit card, line of credit, overdraft, BNPL, etc.

  • Expert proficiency in Python and SQL with hands-on experience in feature engineering, model development, validation, and performance analysis

  • Strong knowledge of statistical modelling techniques, machine learning algorithms, and model deployment in production environments

  • Experience with MLOps platforms (Sagemaker)

  • Track record of measuring and optimizing business outcomes of machine learning models in live production systems

  • Excellent written and verbal communication skills with ability to present complex technical concepts to non-technical stakeholders

  • Experience with regulatory frameworks and model risk management practices in financial services

  • Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, or related quantitative field

  • Passion for applying data science to improve financial products and enhance customer financial outcomes


Description de poste en français


Scientifique de données, Risque de crédit

À propos du poste

On bâtit un produit financier de calibre mondial pis on a besoin de quelqu'un pour amener nos opérations de données au prochain niveau. Notre équipe grandit vite, pis on cherche un·e Modélisateur·trice prédictif·ve pour se joindre à nous. Tu comprends les besoins de prise de décision basée sur les données d'une organisation en forte croissance pis tu te concentres sur des résultats concrets pis des KPIs. Tu cherches la solution à plus fort levier pour les problèmes les plus importants, que ce soit par une analyse pragmatique, un modèle prédictif ou des méthodes d'apprentissage non supervisé.


Ce que tu vas faire

  • Concevoir pis développer des modèles statistiques et d'apprentissage automatique pour les paramètres de risque de crédit (PD, EAD, LGD) à travers les produits de prêt incluant carte de crédit, marge de crédit, découvert, acheter maintenant payer plus tard, etc.

  • Exécuter le cycle de vie complet de développement de modèles, de l'exploration des données pis l'ingénierie des caractéristiques jusqu'à la validation pis le déploiement

  • Implémenter des techniques de modélisation avancées incluant régression, classification, méthodes d'ensemble pis algorithmes d'apprentissage profond

  • Effectuer le suivi de performance des modèles, les tests champion-challenger pis la validation de conformité réglementaire

  • Collaborer avec les équipes Gestion des risques, Crédit pis Produit pour traduire les exigences d'affaires en spécifications techniques

  • Créer des tableaux de bord automatisés, des rapports pis des analyses ad hoc pour soutenir la prise de décision stratégique d'affaires

  • Documenter la méthodologie des modèles, les résultats pis les insights

  • Diriger les initiatives de mise à jour des modèles pis les procédures de back-testing pour maintenir l'exactitude prédictive pis la performance


Qui tu es :

  • 5+ ans d'expérience en modélisation prédictive avec une collaboration démontrée entre les équipes de science des données, d'ingénierie pis de produit

  • Expérience prouvée dans le développement de modèles de risque de crédit (PD, EAD, LGD) pour les produits de prêt aux consommateurs incluant carte de crédit, marge de crédit, découvert, acheter maintenant payer plus tard, etc.

  • Maîtrise experte de Python pis SQL avec expérience pratique en ingénierie des caractéristiques, développement de modèles, validation pis analyse de performance

  • Solide connaissance des techniques de modélisation statistique, des algorithmes d'apprentissage automatique pis du déploiement de modèles en environnements de production

  • Expérience avec les plateformes MLOps (Sagemaker)

  • Historique de mesure pis d'optimisation des résultats d'affaires des modèles d'apprentissage automatique dans des systèmes de production en temps réel

  • Excellentes compétences de communication écrite pis verbale avec la capacité de présenter des concepts techniques complexes à des parties prenantes non techniques

  • Expérience avec les cadres réglementaires pis les pratiques de gestion du risque de modèle dans les services financiers

  • Baccalauréat ou maîtrise en statistiques, mathématiques, économie, informatique ou domaine quantitatif connexe

  • Passion pour l'application de la science des données afin d'améliorer les produits financiers pis les résultats financiers des clients

KOHO is for builders.

If you’re energized by challenge, motivated by mission, and want to be part of a team that punches above its weight - we want to hear from you.

 

The KOHO culture is one of collaboration, creativity, and diverse perspectives. We are committed to building and fostering an inclusive, accessible environment for everyone. If you have any questions, concerns, or requests regarding accessibility needs, please contact [email protected] and the People and Culture team will be happy to help.

 

AI Disclosure: KOHO uses artificial intelligence (AI) in certain aspects of its recruitment process to screen, assess, or select applicants. For any questions or concerns, please contact us at [email protected].

Note: this posting is for an existing vacancy that we are seeking to fill.

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