Application Tracking System (ATS) Parameters

Here is the most common ATS systems in the USA

• NLP algorithms extract key information from resumes:
  • Name, contact info
  • Work history (titles, dates, companies)
  • Education history
  • Skills (technical and soft)
  • Certifications
  • Keywords (specific to the job description)
• Some systems even assess resume formatting and penalize unusual structures.

📊 Keyword & Contextual Matching

• Traditional ATSs used keyword matching: “Does this resume contain the exact phrase ‘Python’?”
• AI systems now use contextual language models (like BERT or domain-specific LLMs) to understand:
  • Synonyms and related skills (e.g., ‘TensorFlow’ implies knowledge of ‘deep learning’)
  • Contextual relevance (e.g., Python used in data science vs. web development)
  • Relative recency and duration of skills in use

📊 Job Description Analysis

• The AI parses the job description itself using NLP to:
  • Identify required and preferred qualifications
  • Understand role seniority and core competencies
  • Cross-reference industry-specific terminology

📊 Scoring & Ranking Candidates

• AI assigns a fit score or ranking based on:
  • Resume-job description alignment
  • Employment gaps (sometimes flagged)
  • Seniority match
  • Location (if remote/hybrid policies apply)
  • Salary expectations (if disclosed)

This returns a matching score, that if a threshold is set, can be used to dynamically filter resumes because the U.S. is in a position that there are more resumes inputted than positions available.

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📌 Excessive Use of Tables

• Problem: Many Canva or Word templates use tables for layout.
• Why ATS struggles: Depending on how the parser reads the document (Word vs. PDF vs. plaintext), tables might:
  • Get flattened improperly
  • Lose hierarchy
  • Merge unrelated content
• Modern AI systems: Handle basic tables but nested or heavily styled tables (with background colors, merged cells) still risk errors.

📌 Unusual Bullet Styles

• Problem: Fancy symbols like ✔️, ✪, ➤, or emoji bullets.
• Why ATS struggles: Most parsers tokenize standard bullets (•, -, *). Non-ASCII symbols can:
  • Be ignored
  • Be replaced by garbage characters
  • Break parsing continuity in older systems

📊 How Traditional (Legacy) Keyword Matching Worked

➡️ Exact match only

• Would look for the precise string Python.
• If your resume said Proficient in scripting languages like Python and Java, it counted as a hit.
• If it said Worked with scripting languages (e.g. Python) — depending on the parser, this might get missed.
• No understanding of synonyms, context, or phrasing variations.

It also typically scored matches based on:

• Frequency (how many times a term appeared)
• Section weighting (keywords in the Experience section might be worth more than in Skills)

📊 How Modern AI/NLP-Driven ATS Keyword Matching Works

➡️ Contextual and semantic matching using NLP models
These systems leverage tools like:

• Named Entity Recognition (NER): to pick out technologies, job titles, company names.
• Word embeddings / transformers (like BERT): to understand relationships between words.

For example:

• Python would be associated with terms like Flask, Django, NumPy, pandas, and concepts like data science, automation scripting.
• The system can infer that developed machine learning models using scikit-learn implies familiarity with Python, even if the word Python isn’t explicitly mentioned.

Modern systems also use lemmatization and stemming:

• Managed, managing, and manage are treated as the same base word manage.

And even phrase detection:

• Recognizing that machine learning is a single skill/keyword rather than two separate unrelated words.

📉 Keyword Pitfalls: What NOT to Do

1. Keyword stuffing: Repeating terms excessively (like listing Python 10 times) can get flagged by some ATS.
2. Keyword spamming: White text on a white background — older trick that modern ATS systems detect and penalize.
3. Using obscure synonyms: Say ‘prognostic model development’ instead of ‘predictive modeling’ — ATS may not link the two unless it’s semantically sophisticated.

📊 Best Practice for Keyword Optimization

• Use language from the job description. If it says SQL, don’t just say relational databases.
• Mirror phrasing and terminology. If they say machine learning engineer, mention both machine learning and engineer explicitly.
• Include a clear ‘Skills’ section listing hard and soft skills.
• Contextualize keywords in your job descriptions. Don’t just dump a list.

📌 Pro Tip:

You can use free tools like:

• Jobscan.co
• Resumeworded.com

To compare your resume against a job description and get a keyword match score.

🔍 Final Thought:

Modern ATS keyword matching is far more sophisticated than it used to be, but it still heavily favors alignment with the job description language. You’re optimizing for both a robot and a human — so clarity and readability matter alongside match rates.

I am looking at engineering a system which parses a master resume, and then creates new content for a resume based on a local GPU using Ollama, and can “spoof” an ATS system. I haven’t been making progress that rapidly.