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 (Computer Vision Engineer)

Razer · JR2025005486

Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work, offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer-centric #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.

Job Responsibilities :

  • Develop and implement computer vision algorithms for tasks such as object detection, recognition, tracking, segmentation, and image classification.
  • Design and architect computer vision systems to meet specific requirements and objectives.
  • Collect, curate, and annotate large datasets of images or videos for training and evaluation of computer vision models.
  • Train, fine-tune, and optimize computer vision models using machine learning frameworks and deep learning libraries.
  • Evaluate the performance of computer vision models using appropriate metrics and benchmarks.
  • Integrate computer vision algorithms and models into larger software systems or products.
  • Conduct testing and validation to ensure the functionality, reliability, and accuracy of computer vision systems.
  • Document design specifications, technical requirements, and implementation details for computer vision solutions.
  • Stay updated on emerging technologies, trends, and advancements in computer vision through research and experimentation.
  • Consider ethical, legal, and regulatory implications in the development and deployment of computer vision systems

  • 开发和实现用于目标检测、识别、跟踪、分割和图像分类的计算机视觉算法。
  • 设计和构建满足特定需求和目标的计算机视觉系统。
  • 收集、整理和标注大量图像或视频数据集，用于计算机视觉模型的训练和评估。
  • 使用机器学习框架和深度学习库训练、微调和优化计算机视觉模型。
  • 使用适当的指标和基准评估计算机视觉模型的性能。
  • 将计算机视觉算法和模型集成到更大的软件系统或产品中。
  • 进行测试和验证，确保计算机视觉系统功能的可靠性和准确性。
  • 编写计算机视觉解决方案的设计规范、技术要求和实现细节的文档。
  • 通过研究和实验，及时了解计算机视觉领域的新技术、趋势和进展。
  • 在开发和部署计算机视觉系统时，考虑伦理、法律和监管方面的影响。

Pre-Requisites :

  • Bachelor’s or master’s degree in computer science, Electrical Engineering, or a related field.
  • Proven experience in developing and implementing computer vision algorithms and models.
  • Proficiency in programming languages such as Python or C++.
  • Strong understanding of image and video processing techniques and methodologies.
  • Familiarity with computer vision libraries such as OpenCV.
  • Experience with data collection, annotation, and preparation for model training.
  • Ability to evaluate and optimize model performance using appropriate metrics and benchmarks.
  • Experience integrating computer vision solutions into software systems or products.
  • Strong problem-solving skills and attention to detail.
  • Excellent communication and teamwork abilitie

  • 计算机科学、电气工程或相关领域的学士或硕士学位。
  • 具有开发和实现计算机视觉算法和模型的经验。
  • 精通Python或C++等编程语言。
  • 深刻理解图像和视频处理技术和方法。
  • 熟悉OpenCV等计算机视觉库。
  • 具有数据收集、标注和为模型训练做准备的经验。
  • 能够使用适当的指标和基准评估和优化模型性能。
  • 具有将计算机视觉解决方案集成到软件系统或产品中的经验。
  • 较强的问题解决能力和对细节的关注。
  • 优秀的沟通和团队合作能力。

Are you game?

View full listing