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03-ats-analysis-engine.md

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03 — ATS Analysis Engine

1. Purpose

Before applying, the system must answer: "Will this resume pass the ATS filter for this specific job?" The ATS Analysis Engine scores, diagnoses, and suggests fixes.

2. How ATS Systems Work (Background)

Modern Applicant Tracking Systems (Workday, Greenhouse, Lever, iCIMS, Taleo) do the following:

  1. Parse the resume into structured fields (name, skills, experience)
  2. Extract keywords from the resume text
  3. Compare extracted keywords against the job requisition
  4. Rank candidates by match percentage
  5. Filter — resumes below a threshold (typically 60–75%) are never seen by a human

Common ATS Pitfalls

Issue Why It Fails
Tables / columns Parser reads cells in wrong order
Headers in images Text inside images is invisible to ATS
Fancy fonts Some fonts don't map to standard Unicode
Missing section headers ATS can't find "Experience" or "Education"
Acronyms only ATS searches for "Machine Learning", not just "ML"
File format Some ATS choke on .docx formatting; plain text or clean PDF is safer

3. Scoring Algorithm

3.1 Input

  • candidate_resume_text — the generated resume text
  • job_description_text — the full JD text

3.2 Scoring Components

Component Weight Method
Keyword Match 40% TF-IDF or embedding similarity of skills/tools
Section Completeness 15% Check presence of: Summary, Experience, Education, Skills
Keyword Density 15% Ratio of JD keywords found in resume vs. total JD keywords
Experience Relevance 15% Semantic similarity of bullet points to JD responsibilities
Formatting Score 15% No tables, standard headers, parseable structure

3.3 Keyword Matching Detail

# Pseudocode
def calculate_keyword_score(resume_text, jd_text):
    jd_keywords = extract_keywords(jd_text)         # spaCy NER + noun chunks
    resume_keywords = extract_keywords(resume_text)

    # Exact matches
    exact_matches = jd_keywords & resume_keywords

    # Fuzzy matches (e.g., "Kubernetes" ≈ "K8s")
    fuzzy_matches = fuzzy_match(jd_keywords - exact_matches, resume_keywords)

    # Semantic matches (embeddings)
    semantic_matches = embedding_similarity(
        jd_keywords - exact_matches - fuzzy_matches,
        resume_keywords,
        threshold=0.85
    )

    total_matched = len(exact_matches) + len(fuzzy_matches) + len(semantic_matches)
    return (total_matched / len(jd_keywords)) * 100

3.4 Output

{
  "overall_score": 78,
  "breakdown": {
    "keyword_match": 82,
    "section_completeness": 100,
    "keyword_density": 71,
    "experience_relevance": 65,
    "formatting": 95
  },
  "missing_keywords": [
    {"keyword": "Terraform", "category": "DevOps Tool", "importance": "high"},
    {"keyword": "CI/CD", "category": "Practice", "importance": "medium"}
  ],
  "suggestions": [
    "Add 'Terraform' to skills section — mentioned 3 times in JD",
    "Include 'CI/CD' in at least one experience bullet",
    "Consider expanding acronym 'ML' to 'Machine Learning'"
  ],
  "formatting_issues": []
}

4. Improvement Suggestions Engine

When the score is below threshold (configurable, default 70%), the system:

  1. Identifies gaps — keywords in JD but not in resume
  2. Searches Candidate Profile — does the candidate actually have this skill?
    • If yes → Suggest adding it. System can auto-include it in next generation.
    • If no → Flag as "Skill Gap" (informational only — won't fabricate).
  3. Re-orders content — move matching bullets higher
  4. Rewrites summary — LLM generates a new summary incorporating missing keywords

Important

The system never fabricates experience or skills. It can only use what exists in the Candidate Profile. If the profile doesn't mention Terraform, it won't add Terraform.

5. Integration Points

flowchart LR
    A[Resume Generator] -->|generated resume text| B[ATS Analyzer]
    C[Job Discovery] -->|job description text| B
    B -->|score + suggestions| D[Decision Engine]
    D -->|score >= threshold| E[Proceed to Apply]
    D -->|score < threshold| F[Re-generate with suggestions]
    F --> A
Loading

The analyze → generate → re-analyze loop runs at most 2 iterations to avoid infinite loops. If the score is still low after 2 passes, the job is flagged for manual review.