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 .

Head of Data

AMCS Group · Limerick, IE

Who we are

Sustainability software specialist, AMCS, is headquartered in Ireland, with offices in Europe, the USA, and Australasia. With over 1,300 highly skilled employees across 22 countries, we specialize in delivering technology solutions to facilitate a carbon neutral future.

What we do

Our innovative SaaS solutions increase efficiency and boost sustainability in resource-intensive industries. Over 5,000 customers across 23 countries already benefit from our Performance Sustainability software, ensuring we deliver practical solutions for improved profitability and environmental resilience across the globe.

The role

AMCS is hiring a Head of Data to lead the company’s transformation toward becoming truly data-driven. Reporting to the CFO, you will build and lead a central Data team responsible for establishing AMCS’s enterprise-wide data architecture, data operating model, governance, and standards for business data. You will partner closely with lines of business and functional leaders to enable trusted, well-governed data products, self-service insights, and consistent metrics across the company.

This role blends strategy, execution, and change leadership. You will define “how AMCS does data” and drive adoption across the organization.


Key Responsibilities

  • Define and lead AMCS’s enterprise data strategy, setting the architectural direction, governance model, and operating model for all business data.

  • Build an enterprise-grade data platform, starting with Finance, expanding to Customer 360, and later integrating product/usage data to create a unified view of the customer and business.

  • Enable faster and higher‑quality insights, reduce data fragmentation, and drive adoption of consistent KPIs, metrics, and standardized reporting across AMCS.

  • Deliver outcomes fast — investors and CFO emphasise a strong execution rhythm and pragmatism.

  • Serve data needs across the entire business, not just Finance or Technology, while partnering closely with lines of business (Sales, CS, Operations, Product, Finance, IT/Security).


What you will own

Enterprise Data Strategy & Roadmap

  • Define and execute AMCS’s enterprise data strategy aligned to business priorities (growth, operational efficiency, product, customer outcomes).

  • Build a pragmatic multi-year roadmap: foundational capabilities first (platform, governance, quality), then acceleration (advanced analytics, experimentation, AI/ML enablement).

  • Establish measurable outcomes and track progress (e.g., time-to-insight, data quality SLAs, adoption of standardized metrics).


Target Data Architecture & Platform Direction

  • Define the target-state data architecture (e.g., modern warehouse/lakehouse patterns; domain-oriented “data products” where appropriate).

  • Set standards for ingestion, transformation, orchestration, metadata/lineage, semantic layers, observability, and secure access. Ensure the platform enables both enterprise reporting and business-unit agility (self-service with guardrails).


Data Governance, Ownership & Decision Forums

  • Establish and operationalize an enterprise Data Governance Framework covering policies, roles, and controls.

  • Define clear accountability via a data ownership model: data owners (business accountability) and data stewards (day-to-day management).

  • Set up and lead a Data Governance Council (and working groups) to drive prioritization, resolve issues, and ensure cross-company alignment.

  • Implement a scalable approach to privacy, security, and compliance in partnership with IT/Security and Legal.


Data Quality, Master Data & Common Definitions

  • Implement data quality management: quality dimensions, monitoring, SLAs, incident management, and continuous improvement.

  • Define approach for master/reference data and core business entities (customers, products, contracts, revenue, usage, etc.).

  • Create consistent definitions of key KPIs and metrics (a trusted “single source of truth”) through a governed metrics layer.


Business Partnership & Enablement

  • Partner with leaders across Finance, Sales, Customer Success, Product, Operations, and Technology to identify and deliver high-impact use cases.

  • Enable self-service analytics through curated datasets, documentation, data cataloging, and user enablement/training.

  • Drive adoption: communication plans, stakeholder engagement, and consistent ways of working across central and business teams.


Leadership & Team Building

  • Build and lead a high-performing central data function (data architecture, engineering, governance, analytics enablement).

  • Recruit, develop, and mentor talent; set an execution cadence and delivery standards.

  • Manage cross-functional stakeholders and deliver outcomes with executive-level communication and transparency.


What We’re Looking For

Required Experience & Capabilities

  • Senior leadership experience in Data / Analytics / Data Engineering / Data Platform (e.g., Head of Data, Director of Data, Data Platform Lead).

  • Proven track record delivering modern data architecture and building enterprise data capabilities in a multi-stakeholder environment

  • Demonstrated success establishing data governance (frameworks, councils/forums, ownership models, quality management).

  • Strong understanding of modern data practices and components (warehouse/lakehouse concepts, ELT/ETL, orchestration, semantic/metrics layers, catalogue/lineage, observability, access controls).

  • Strong stakeholder management and change leadership, able to drive adoption, accountability, and cultural shift.

  • Business/value orientation: prioritizes work that delivers measurable outcomes.


Nice to Have

  • Experience in enterprise SaaS or multi-product organizations.

  • Experience enabling advanced analytics or AI/ML initiatives through robust data foundations.

  • Familiarity with domain-oriented “data product” approaches and pragmatic federated models.

Location / Working Model

AMCS supports flexible working arrangements depending on role requirements and location. Occasional travel to key AMCS sites and stakeholders may be required.