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

Madhive · Remote, United States

Madhive is the leading independent and fully customizable operating system built to help local media professionals build profitable, differentiated, and efficient businesses. Madhive empowers sales teams to extend their reach into streaming and connects local advertisers with the communities they serve. Madhive’s platform provides the unique ability to reach local audiences at national scale, with premium supply partnerships and end-to-end tools for planning, targeting, and measuring full-funnel campaign outcomes. Powering campaigns for over 30,000 small and medium businesses per day, Madhive is driving the evolution of local media. 

Madhive is currently seeking a Senior Data Scientist to join its growing team. This is a very exciting time to join Data Science at Madhive. We’re partnering with our engineering and product teams to create and scale best-in-class, TV-first solutions in the market. The Data Science team is directly responsible for the advanced data solutions and models that directly contribute to the company’s revenue and competitive advantage.

The ideal candidate is an expert in the ad-tech ecosystem, possessing deep technical and theoretical knowledge in programmatic media, Connected TV (CTV), and data-driven audience strategies. You will be responsible for designing and implementing our next generation of attribution and optimization products, with a strong focus on advanced statistical modeling techniques.

What you’ll do:

  • Architect and implement machine learning models for campaign optimization, focusing on bid strategies, budget allocation, and pacing algorithms within the DSP environment for programmatic campaigns with focus on CTV

  • Lead the design of our model orchestration layer to solve complex multi-objective optimization challenges. Working at the intersection of Product and Engineering, you will ensure our model suite operates in sync to maximize performance across conflicting business KPIs

  • Drive data and ML strategy for audience segmentation and targeting capabilities, utilizing first- and third-party data sources to enhance product performance and advertiser outcomes.

  • Collaborate closely with Product and Engineering teams to integrate new models and data pipelines into production systems, ensuring scalability and performance.

  • Conduct deep-dive analytics to uncover strategic insights into campaign performance, market trends, and audience behavior, translating findings into actionable product recommendations.

  • Serve as a subject matter expert in ad-tech data science for internal and external stakeholders, presenting complex methodologies and results clearly.

  • Mentor junior data scientists and contribute to the team's culture of rigorous experimentation, statistical excellence, and innovative problem-solving.

Who you are:

  • Master’s or Ph.D. Degree in Computer Science, Statistics, Economics, or a hard science field preferred.

  • 7+ years of professional experience in a Data Scientist role, with a significant focus on the advertising technology (ad-tech) space.

  • Deep, demonstrable expertise in DSPs, programmatic advertising, and CTV measurement.

  • Proven track record in developing and deploying models for bidder optimization and attribution/measurement.

  • Fluent in advanced statistical and machine learning techniques, with a strong command of Python and SQL.

  • Nice to have: Experience with GCP and the Vertex AI ecosystem, as well as other tools such as feature stores and experiment tracking.

  • Nice to have: experience with LLMs and agentic AI frameworks/solutions, with special focus on evaluation.

  • Strong communication skills, with the ability to articulate technical concepts and complex model results to non-technical stakeholders.

  • Takes an innovative and research-oriented approach to problem-solving, with a focus on delivering high-quality, scientifically sound solutions.

The approximate compensation range for this position is $180,000-$200,000. The actual offer, reflecting the total compensation package and benefits, will be determined by a number of factors including the applicant's experience, knowledge, skills, and abilities, as well as internal equity among our team.

#LI-Remote

We are Madhive

Madhive is a dynamic, diverse, innovative, and friendly place to work. We embrace our differences and believe they fuel our creativity. We come from varied backgrounds and think that’s important. Whether it’s taking ideas from previous lives and applying them in different ways or creating something completely new, we are all trail-blazing team players who think big and want to make an impact. 

We are committed to cultivating a culture of inclusion and collaboration. We welcome diversity in education, culture, opinions, race, ethnicity, gender identity, veteran status, religion, disability, sexual orientation, and beliefs.

Please be advised that we will NOT be using third-party recruiting agencies for this search.