Data · Free AI Resume Roast

Data Analyst Resume
Brutally Roasted

You turn data into decisions. Your resume turns decisions into 'supported analyses.' The passive voice is doing more work than you did.

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Common Mistakes on Data Analyst Resumes

The recurring patterns recruiters see — and reject.

01
Every bullet starts with 'analyzed', 'pulled', 'built', 'created' — with no decision changed.
02
Listing SQL, Python, R, Excel, Tableau, Power BI — all at 'proficient.' Depth unclear.
03
No mention of data scale: row counts, systems joined, complexity of the model/dashboard.
04
Dashboards 'built' with no users, adoption, or business decisions influenced.
05
Zero business-side fluency — what was the question, and who asked it?

Real roasts our AI writes for this role.

“You 'built dashboards that provided insights.' The dashboard's emotional state is not the metric.”

“Your resume says 'analyzed large datasets' like that's a flex. How large? What database? What question?”

“'Collaborated with stakeholders' is analyst for 'I went to meetings and nothing came of it.'”

Specific moves that actually move the grade.

✓
Every bullet: the question you were asked → analysis done → decision or dollar outcome.
✓
Quantify data scale: rows, tables joined, data sources — complexity is scope.
✓
Call out adoption: dashboards used daily by N teams, or analyses referenced in leadership decks.
✓
List tools with depth markers: 'complex SQL (CTEs, window functions)', 'Tableau (LoD calcs).'
✓
Include a before/after: metric baseline → analysis → action taken → new metric.

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Data Scientist Resume Roast

Common mistakes + specific fixes →