RESUME MISTAKES
Turn Data Scientist Resume Flaws into Hiring Wins
Identify and correct the most common mistakes that keep you from landing your next data role.
How This Page Helps
Help data scientists create error‑free, ATS‑optimized resumes that showcase impact and technical expertise.
Spot hidden ATS blockers
Learn quantifiable phrasing
Showcase the right technical stack
Structure your resume for readability
Apply proven formatting standards
Common Mistakes That Kill Your Chances
Each mistake includes why it hurts, how to fix it, and before/after examples
Vague Objective StatementMEDIUM
Why it hurts
- Provides no value to recruiters
- Consumes prime resume space
- Fails ATS keyword matching
How to fix
- Replace with a concise professional summary
- Highlight years of experience and key achievements
- Include relevant data science keywords
❌ Before
Objective: Seeking a challenging position in a dynamic company.
✓ After
Data Scientist with 4+ years of experience building predictive models that increased revenue by 12%.
ATS Tip
Use role‑specific keywords like 'machine learning', 'predictive modeling', and 'Python'.
Detection Rules
Contains the word 'objective'
Length < 30 words
Lacks quantifiable metrics
Resumly Tip
Swap the objective for a summary that sells your impact.
Missing Quantifiable ImpactHIGH
Why it hurts
- Hard to gauge your contributions
- ATS often looks for numbers
- Recruiters prefer results-driven language
How to fix
- Add percentages, dollar amounts, or time saved
- Tie each project to business outcomes
- Use action verbs with metrics
❌ Before
Developed machine learning models for customer churn.
✓ After
Developed a churn prediction model that reduced customer loss by 15%, saving $200K annually.
ATS Tip
Include numeric values and relevant metrics.
Detection Rules
Bullet points lack numbers
Sentences start with 'Developed' without metrics
Resumly Tip
Quantify every achievement to demonstrate value.
Overloading with JargonLOW
Why it hurts
- Confuses non‑technical recruiters
- Reduces readability
- ATS may not recognize obscure acronyms
How to fix
- Explain complex terms in plain language
- Prioritize widely‑known tools and frameworks
- Balance technical depth with clarity
❌ Before
Implemented a CNN‑LSTM hybrid using TensorFlow‑GPU for time‑series forecasting.
✓ After
Built a deep learning model with TensorFlow that improved forecast accuracy by 8%.
ATS Tip
Use common skill names like 'TensorFlow', 'Python', 'SQL'.
Detection Rules
Contains acronyms without explanation
Sentence length > 25 words
Resumly Tip
Simplify technical descriptions for broader audiences.
Unclear Project SectionMEDIUM
Why it hurts
- Recruiters can’t see relevance
- ATS may miss key skills
- Projects appear as a dump of text
How to fix
- Use a consistent format: Project Title, Tools, Outcome
- Start each bullet with an action verb
- Highlight the problem, solution, and impact
❌ Before
Project: Predictive Analytics. Used Python, pandas, scikit-learn. Results: improved predictions.
✓ After
Predictive Analytics for Sales Forecasting – Python, pandas, scikit-learn – Increased forecast accuracy from 70% to 85%.
ATS Tip
Include tool names as keywords.
Detection Rules
Project entries lack structure
Missing tool list
No outcome metric
Resumly Tip
Structure each project for maximum impact.
Improper File Format & NamingHIGH
Why it hurts
- ATS may not parse PDFs with complex layouts
- Hiring managers may overlook poorly named files
- File may be rejected automatically
How to fix
- Save as PDF/A or simple .docx
- Name file as FirstLast_DataScientist.pdf
- Avoid graphics that hinder parsing
❌ Before
Resume_JohnDoe.doc
✓ After
JohnDoe_DataScientist.pdf
ATS Tip
Use standard fonts and simple formatting.
Detection Rules
File extension not .pdf or .docx
File name contains spaces or special characters
Resumly Tip
Follow naming conventions to ensure ATS readability.
Formatting Guidelines
File Types: PDF, DOCX
Sections: Header, Professional Summary, Technical Skills, Professional Experience, Projects, Education, Certifications, Publications
Naming: FirstLast_DataScientist.pdf
Consistency
Length: 1‑2 pages for most data scientists; 2 pages if >10 years experience
Date Format: MM/YYYY
Location Format: City, State (or Country)
Resume Quality Checklist
- Use a concise summary with keywords
- Quantify every achievement
- List technical skills in a dedicated section
- Structure projects with tools and impact
- Save as PDF/A with proper file name
- Proofread for spelling and grammar
- Ensure consistent date formatting
ATS Alignment Guide
Common ATS Systems: Lever, Greenhouse, iCIMS, Workday, SmartRecruiters
Keyword Strategy: machine learning, statistical modeling, Python, R, SQL, data visualization, A/B testing
Heading Format: Use standard headings like 'Professional Experience' and 'Technical Skills' to match ATS parsing.
Quick Fix Workshop
Paste your current resume text
- Replace objective with summary
- Add metrics to bullet points
- Standardize section headings
- Convert file to PDF/A
- Rename file per convention
Ready to transform your data scientist resume? Get our free fix guide now!
Download the Guide
