Why I Stopped Trusting Resumes With 10+ Skills Listed

Interview Insights · Skill Claims

Why I Stopped Trusting Resumes With 10+ Skills Listed

SECTION 01The pattern behind long skill lists

Here is a skills box I see in some form almost every week: Python, SQL, Excel, Power BI, Machine Learning, Deep Learning, NLP, Statistics, Data Visualisation, Communication, Leadership, Problem Solving, Teamwork. Thirteen entries. Then I read the projects: a sales dashboard and an Excel report. Machine learning, deep learning and NLP do not appear anywhere else. Neither does leadership.

This is not usually dishonesty. Candidates list what they have studied, or what job descriptions mention, hoping it helps them pass filters. But to a human reviewer, a skill with no evidence reads as a skill that may not exist. And once I find two or three of these, I start doubting the skills that are real.

HOW I SCAN A RESUME (about a minute)

1. Count skills in the skills box.
2. For each skill, search the projects and
   experience sections for it being USED.
3. Mark each skill:
     E = evidence found
     W = weak mention only
     N = no evidence
4. If N appears more than a couple of times,
   I lower my trust in the E skills too.

Example:
Python E  SQL E  Excel E  Power BI E
ML N  Deep Learning N  NLP N  Leadership N

SKILLS (each one appears in a project below)

SQL        joins, CTEs, window functions
Excel      SUMIFS, XLOOKUP, pivot tables
Power BI   data model, DAX measures
Python     Pandas cleaning, groupby
Statistics A/B test with a two-proportion z-test

Removed: Machine Learning, Deep Learning, NLP
(moved to 'Currently learning' until a project exists)
Removed: Leadership, Teamwork, Communication
(shown through a project story instead)

The fixed version has five skills, each with evidence. It looks smaller on paper, but in an interview it is much stronger, because every claim survives the first question.

SECTION 02Proving the technical skills you do list

Once you cut the unsupported skills, make the remaining ones easy to verify. The best way is a direct link in the reader's mind between a skill and a project bullet. If you list statistics, there should be a bullet where you used a statistical method for a reason. If you list Python, there should be a bullet where Python solved a problem Excel could not handle comfortably.

Interviewers often test the most impressive-sounding skill on your list. For many analyst resumes, that is statistics. Be ready to explain the test you used, why you chose it and how you interpreted the result in plain words.

from statsmodels.stats.proportion import proportions_ztest

# Sign-ups and visitors for two page versions
# (practice data from a class project)
signups  = [120, 150]   # version A, version B
visitors = [2400, 2500]

stat, p_value = proportions_ztest(signups, visitors)
print(f'z = {stat:.2f}, p = {p_value:.3f}')

# Spoken interpretation:
# 'If the two versions truly converted the same, a gap
# this large would be fairly unlikely. So the data leans
# toward B, but I would still check the sample was split
# randomly and run it for a full week.'

-- Keep the latest record per customer
WITH ranked AS (
  SELECT *,
         ROW_NUMBER() OVER (
           PARTITION BY customer_id
           ORDER BY updated_at DESC
         ) AS rn
  FROM customers_raw
)
SELECT *
FROM ranked
WHERE rn = 1;

-- Why not DISTINCT? Rows differ in updated_at and
-- other columns, so DISTINCT keeps all versions.
-- ROW_NUMBER lets me choose which version to keep.

Both tabs pair code with a spoken explanation. That pairing is what an interviewer wants to hear: you can use the skill, and you can say in plain language what it did and why you chose it over an alternative.

SECTION 03Showing soft skills without listing them

Communication, leadership, teamwork and problem solving are the most listed and least trusted skills on fresher resumes. Everyone has them in the skills box, so they carry no weight. Yet they genuinely matter for analysts, who spend much of their time explaining findings to people who do not read SQL.

The answer is to show soft skills through evidence elsewhere: a bullet about presenting a dashboard to a manager, a project where you coordinated with teammates, a story about simplifying a report for a non-technical audience. These say far more than the word 'communication' ever could.

Q: Tell me about a time you explained data to
someone non-technical.

A: During my internship the store manager found my
dashboard confusing. It had eight charts. I asked
what decision she made each week, and it was mainly
which products to reorder. I rebuilt the first page
around that: one table of low-stock fast sellers and
one trend chart. She started using it in her weekly
meeting, and I moved the other charts to a detail page.

INSTEAD OF A SOFT SKILLS LIST, WRITE BULLETS LIKE:

- Rebuilt a dashboard's first page around the store
  manager's weekly reorder decision
- Coordinated a three-person college project by
  splitting cleaning, analysis and dashboard work
- Explained an A/B test result to classmates without
  statistical jargon in a short presentation
- Organised the analytics club's data challenge and
  wrote the problem statements

The communication story never uses the word 'communication', yet it proves the skill completely. It shows listening, simplifying and adapting to a user. That is what the interviewer wanted to learn when they asked.

SECTION 04Build your skill evidence matrix

This matrix turns your skills section into something you can defend line by line. Fill it in honestly, cut what has no evidence and use the result to rewrite your resume. Uncodemy trainers often use a similar matrix in resume review sessions.

SKILL EVIDENCE MATRIX

Skill      | Where it is used     | Specific feature | Demo live?
-----------|----------------------|------------------|-----------
SQL        | Retail project       | ROW_NUMBER dedup | Y
Excel      | Internship report    | SUMIFS, pivots   | Y
Statistics | A/B class project    | z-test           | Y
ML         | (none)               |                  | N -> move
Leadership | Analytics club       | ran challenge    | story

Rule: no evidence -> remove or 'Currently learning'.

REWRITE IN FIVE STEPS

1. List every skill on your current resume.
2. Fill the evidence matrix for each one.
3. Remove skills with no evidence, or move them to
   'Currently learning'.
4. Add the specific feature next to each kept skill.
5. Turn soft skills into bullets or stories in
   projects and activities.

Target: five to seven technical skills, all evidenced.

PRACTISE ONE PROBE PER KEPT SKILL (aloud)

SQL:        'Why ROW_NUMBER and not DISTINCT?'
Excel:      'SUMIFS vs a pivot table: when each?'
Power BI:   'Why do you need a date table?'
Python:     'How did you handle mixed date formats?'
Statistics: 'Explain your p-value in plain words.'

Answer each in under a minute. Record and review.

SECTION 05Habits that keep your skill claims credible

Credible skill sections are maintained, not written once. These habits keep yours honest and strong.

• Add skills after projects — Only add a new skill to your resume once you have used it in a project you can explain, not when you finish a course.
• Use a learning line — Put skills you are studying under 'Currently learning', which shows growth without inviting a deep test.
• Name the feature — Write the specific method next to each skill, such as window functions for SQL, so the claim is concrete.
• Retire generic labels — Drop broad entries like 'data visualisation' and 'problem solving' and show them through project bullets instead.
• Re-check before each application — Run your evidence matrix again before applying, especially if you tailored the skills to a new job description.

SECTION 06Interview Q&A — Skill claims

SECTION 07Test yourself — Skill claims quiz

SECTION 08Frequently asked questions

SECTION 09Related reads