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 .

Quantitative AI Strategist

DRW · New York

Regular Immediate Trading

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

We are seeking a Quantitative AI Strategist to join our quantitative analytics team. This is a front-office role at the intersection of quantitative finance, AI, and product development — focused on building and evolving the firm’s AI-powered research and analytics platform.

The platform helps traders, researchers, analysts, and risk managers move from questions to actionable insight by unifying analytics, data, and research. Your job is to make it indispensable — by working directly with trading desks to understand their workflows, building the quantitative and AI capabilities they need to generate better ideas and make better decisions, and partnering with software engineers to deliver them at production quality.

You will have broad exposure across asset classes, desks, and problem types — from signal generation and backtesting to risk analysis and research analytics — while working at the frontier of applying AI to quantitative finance. The ultimate goal is to help the firm generate more revenue through AI-assisted trading and research.

The ideal candidate will be able to:

  • Work directly with trading desks across asset classes and other stakeholders across the firm to identify high-value use cases for the platform.
  • Determine the right balance between AI autonomy and structured tooling — deciding what the AI should reason through on its own, what instructions and domain knowledge it needs, and what purpose-built code it should call — and build accordingly.
  • Work with front-office stakeholders to turn desk needs into well-defined quantitative problems/workflows, and collaborate with technology teams and quantitative researchers to deliver solutions.

Key Responsibilities:

  • Prototype and validate quantitative workflows end-to-end — from data retrieval and signal construction through to strategy evaluation, PnL simulation, testing, and risk/scenario analysis — while defining how the AI should interact with data sources, analytics libraries, desk-specific tools, etc., and work with engineers to deliver them as production platform capabilities.
  • Write high-quality platform code and quantitative libraries — including code designed to be called and understood by AI, with clear interfaces, documentation, and instructions to AI.
  • Enhance the platform’s ability to reason about markets, interpret financial data, and produce reliable, contextually aware analysis across products and markets.
  • Continuously evaluate how the platform is used, identify where it excels and where it falls short, and drive improvements that deliver measurable value to trading and research workflows.
  • Engage with stakeholders across the firm — trading desks, risk management, researchers, new joiners, and others — to discover emerging use cases and adapt the platform’s capabilities accordingly.
  • Proactively identify new use cases and capabilities as AI technology evolves.
  • Act as the first line of quantitative support for platform users — diagnosing issues, feeding insights back into platform development, and ensuring a high-quality user experience.

Qualification and Experience:

  • Background in quantitative finance, financial engineering, applied mathematics, statistics, physics, computer science, or a related technical field.
  • 3–7 years’ experience in a front-office quant, strategist, or quantitative research role, ideally with exposure to multiple asset classes.
  • Solid understanding of financial markets, pricing/risk methodologies, and PnL attribution.
  • Experience building or contributing to internal analytics platforms or tools used by traders and researchers.
  • Experience with signal generation, backtesting, or systematic strategy development.
  • Strong programming skills in Python. Familiarity with Git and collaborative development workflows.
  • Familiarity with AI technologies and their application to quantitative workflows is a strong plus.
  • Experience building AI agents is a strong plus.
  • Excellent communication skills — able to engage directly with trading desks to understand their needs, formalize them into quantitative specifications, and collaborate effectively with software engineers.
  • Strong problem-solving ability, intellectual curiosity, and comfort working across team boundaries in a fast-paced trading environment.
  • Strong ability to quickly learn and adapt to new technologies — particularly important given the rapid pace of development in AI.

The annual base salary range for this position is $175k to $250k depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus. In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

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