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 .

Voir l'annonce

INSERM

Description

Poste ingénieur traitement de signaux d’électroencéphalographie (EEG) et apprentissage machine, Lille


Missions


·       Développer des pipelines de traitement du signal EEG (prétraitement, filtrage, extraction de caractéristiques).


·       Développer, entraîner et évaluer des modèles d’apprentissage automatique et/ou profond (ML/DL) appliqués aux données EEG.


·       Mettre en œuvre des méthodes de classification, régression ou détection d’événements (ex. états cognitifs, pathologies, intentions motrices).


·       Optimiser les performances des modèles (feature engineering, sélection de modèles, validation croisée).


·       En lien avec des ergonomes, proposer une interface homme-machine pour l’utilisation en clinique.


·       Intégrer les développements dans un logiciel stand-alone.


·       Assurer la documentation, la traçabilité et la reproductibilité des développements.


Compétences requises


·       Maîtrise du traitement du signal (filtrage, FFT, ondelettes, analyses temps-fréquence).


·       Connaissance des signaux EEG (artefacts, montage, référencements, normes).


·       Maîtrise des algorithmes de machine learning (SVM, forêts aléatoires, k-NN, réseaux de neurones).


·       Notions en deep learning (CNN, RNN, Transformers) appliqué aux séries temporelles.


·       Programmation scientifique en Python (NumPy, SciPy, MNE, scikit-learn, PyTorch/TensorFlow).


·       Connaissance des bonnes pratiques de développement.


Compétences appréciées


·       Expérience en interfaces cerveau-machine (BCI).


·       Notions de réglementation médicale et de données de santé (RGPD, dispositifs médicaux).


·       Notions de statistiques et d’évaluation de modèles.

Profil recherché

Profil


·       Diplôme Bac+5 minimum (école d’ingénieur, master) en traitement du signal, data science, intelligence artificielle, biomédical ou neurosciences.


·       Expérience professionnelle ou académique dans l’analyse EEG et/ou la data science.