Test your resume against a job description like a recruiter

How a recruiter search actually looks

On most job portals and in most applicant tracking systems, a recruiter types a few terms, often combined with AND, OR and NOT, and adds filters for location, experience and notice period. A typical search for an analyst role might be:

("data analyst" OR "MIS analyst") AND SQL AND ("power bi" OR powerbi) AND excel

Two consequences follow. First, a missing term can remove you from the results entirely, not just rank you lower. Second, the recruiter chooses the terms, usually from the job description in front of them, so the posting is your best guide to the vocabulary.

The manual method in four passes

Pass 1, extract. Print or copy the job description. Highlight every tool, technique and task noun: SQL, Advanced Excel, Power BI, DAX, MIS reports, root cause analysis, stakeholders, A/B testing.

Pass 2, list variants. For each term, write the forms someone might search. "Power BI" and "PowerBI". "A/B testing" and "experiments". "MIS" and "management information system".

Pass 3, find. Search your resume for each variant (Ctrl+F in the PDF is fine). Mark each term yes or no.

Pass 4, classify each no.

A script that does passes 3 and 4 for you

If you apply to many roles, the manual check gets tedious. This short Python script uses only the standard library. The job description and resume are synthetic examples; paste your own text into the two strings.

import re

# Synthetic job description and resume text. Replace with your own.
jd = """We are hiring a Data Analyst. Must have strong SQL and Advanced Excel.
Experience with Power BI dashboards and DAX is required. Knowledge of Python
(pandas) is a plus. You will build MIS reports, perform root cause analysis and
present insights to stakeholders. Familiarity with A/B testing preferred."""

resume = """Data Analyst trainee. Wrote SQL queries with joins and window functions.
Built a PowerBI dashboard for returns. Used Excel pivot tables and XLOOKUP.
Cleaned data in Python using pandas. Presented findings to the store team."""

# Each skill maps to the spellings a recruiter might search or you might write.
skills = {
    "SQL": ["sql"],
    "Advanced Excel": ["advanced excel", "excel"],
    "Power BI": ["power bi", "powerbi"],
    "DAX": ["dax"],
    "Python": ["python"],
    "pandas": ["pandas"],
    "MIS reports": ["mis"],
    "Root cause analysis": ["root cause"],
    "Stakeholders": ["stakeholder"],
    "A/B testing": ["a/b test", "ab test", "experiment"],
}

def found(text, variants):
    text = text.lower()
    hits = []
    for v in variants:
        if re.search(r"(?<![a-z])" + re.escape(v) + r"(?:s|ing|ed)?(?![a-z])", text):
            hits.append(v)
    return hits

print(f"{'Skill':<22}{'In JD':<8}{'In resume':<12}Resume spelling")
for skill, variants in skills.items():
    in_jd = bool(found(jd, variants))
    hits = found(resume, variants)
    if not in_jd:
        continue
    print(f"{skill:<22}{'yes':<8}{('yes' if hits else 'NO'):<12}{', '.join(hits)}")

Output:

Skill                 In JD   In resume   Resume spelling
SQL                   yes     yes         sql
Advanced Excel        yes     yes         excel
Power BI              yes     yes         powerbi
DAX                   yes     NO          
Python                yes     yes         python
pandas                yes     yes         pandas
MIS reports           yes     NO          
Root cause analysis   yes     NO          
Stakeholders          yes     NO          
A/B testing           yes     NO          

The regular expression matches whole words and allows simple endings like "s" or "ing", so "stakeholder" also catches "stakeholders".

Reading the result honestly

The script is only a checklist. Here is how the fictional candidate should act on each gap:

• Advanced Excel: matched only "excel". Her XLOOKUP and pivot work qualifies, so she changes the skills line to "Advanced Excel (pivot tables, XLOOKUP, Power Query)".
• Power BI: matched, but through "PowerBI". She changes to the posting's spelling.
• DAX: she wrote two measures in her dashboard project but never said so. She adds "wrote DAX measures for net revenue and return rate".
• Stakeholders: she did present to a store team. She rewrites that bullet: "Presented findings to store stakeholders".
• MIS reports and root cause analysis: she has done neither in a real sense. She leaves them out and prepares to say so honestly if asked.
• A/B testing: "preferred", not required, and she has not done it. She leaves it out.

Final coverage: eight of ten, with every match backed by a real bullet. That is a better position than ten of ten with two claims she cannot defend.

Limits of keyword matching

• A resume stuffed with terms and no evidence may pass the search and fail the human read, which usually takes well under a minute.
• Filters you cannot see (location, notice period, experience range) can exclude you even at 100% keyword match. Keep portal profile fields accurate, as described in our Naukri profile guide.
• Different tools handle spelling, stemming and phrases differently. The script approximates this; it does not replicate any specific system.

For a quicker first pass, the free skill check compares your resume with a target role and lists matched and missing skills. Then use the line-by-line method in tailoring a resume to a job description to rewrite the bullets. If DAX and Power BI are the recurring gaps, the Data Analyst programme builds both through project work.