Good vs. bad data scientist resume examples
How Data Scientists like RS go from 56 to 90 on ATS — the mistakes that drag scores down, and the fixes that landed real interviews.
Writing a resume means making the right decisions. Whether it's style, formatting, or content, resume examples can help you understand whether you're making good choices that will move your data career forward. Checking out examples of good vs. bad resumes can help you understand the most common resume mistakes and the best strategies for impressing employers and landing the interview.
In this article, we'll cover:
- Three bad data scientist resume examples with notes on how they can be improved
- A good data scientist resume example that fixes those mistakes
- Tips for improving your resume
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3 bad resume examples and what they do wrong
Before we get into what makes a "bad" resume, it's important to understand what a resume needs to do: grab attention in 7 seconds, describe your qualifications clearly, and pass through applicant tracking systems (ATS).
Bad data scientist resume example #1: inconsistent and generic
- Inconsistent format — uneven spacing, mixed column counts, sloppy whitespace.
- Unprofessional font choice and inconsistent sizes.
- Spelling mistakes that signal a rushed application.
- Generic content with no specific skills or achievements.
- Outdated "References available upon request" line.
- Casual email address like firstnamelastname123@example.com.
- Length — kept to a single concise page, which is the right call for this experience level.
Bad data scientist resume example #2: pretty template, hollow content
- Filler content that focuses on irrelevant details instead of role-specific impact.
- Lists vague responsibilities ("responsible for data tasks") with zero metrics or outcomes.
- Professional template with consistent formatting that looks polished at first glance.
- Healthy blend of soft skills alongside technical skills.
Bad data scientist resume example #3: close, but missing the impact
- Includes a personal photo — almost always a bad idea for U.S. applications.
- Experience is more targeted, but no measurable outcomes (numbers, percentages, dollar figures).
- Missing optional sections like certifications, volunteer work, or languages that would add depth.
- Professional, easy-to-read template.
- Active, energetic verbs throughout the experience section.
- Balanced skills section with both hard and soft skills.
What a data scientist resume that scores 90/100 looks like
This is the version RS submitted after the rewrite — the same one shown above.
- Every standard section in its place, formatted consistently from top to bottom.
- Clean template that guides the reader through a clear data career story.
- Keywords pulled directly from data scientist job descriptions — both hard skills and soft skills.
- Portfolio / profile link for instant credibility.
- Language proficiency listed where relevant to show communication range.
- Numerical data in nearly every bullet — real figures backing up real impact.
Tips for improving your data scientist resume
Rethink your formatting
Most resumes use the reverse-chronological format. If you're entry-level or changing careers, try the functional or combination format instead.
Don't be afraid to branch out
Use optional sections (projects, certifications, volunteer work, languages) to show what makes you stand out — as long as they're relevant.
Make it targeted
Read each job description closely and weave in 8–12 data scientist-relevant keywords naturally so ATS sees you as a clear match.
Pick the right template
Choose a premade, ATS-friendly template that's organized, scannable, and aligned with your personal brand.
Key takeaways
Understand your resume's purpose
It needs to grab attention, highlight qualifications, and pass ATS screening — all at once.
Make strategic style choices
A polished template and the right format put your strongest qualifications front and center.
Sweat the details
Typos, awkward fonts, and unprofessional emails make the whole resume read as sloppy. Fix the small things.
Focus on achievements, not duties
Skip generic responsibilities — emphasize accomplishments and the value you added.
Include measurable data
Numbers and metrics turn claims into proof. Use them wherever you honestly can.
FAQ
What makes a resume "bad"?+
A bad resume fails to make the candidate's strengths stand out — usually because of inconsistent formatting, generic content, missing metrics, or ATS-unfriendly structure.
Should I include "References available upon request" on my resume?+
No. It's assumed and wastes a line you could spend on a stronger accomplishment bullet.
What's the biggest content mistake people make on their resumes?+
Listing duties instead of achievements. Recruiters can find a job description anywhere — they want to see the specific outcomes only you delivered.
What's wrong with flashy templates or multiple columns?+
Most multi-column and graphic-heavy templates break ATS parsing, so half your content never enters the searchable index. Stick to single-column, semantic layouts.
Does my resume's file name matter?+
Yes — use something like FirstName-LastName-Resume.pdf. It's easier for recruiters to find and looks more professional than resume-final-v3.pdf.