ATS RESUME CHECKER

ATS Resume Checker for Data Science Intern

Data science internship screens look for programming, statistics and applied machine learning with real data. This page scores your resume against that profile. It runs in your browser; your file is never uploaded.

Target job title Your resume fileUpload a resumeDrop a PDF, DOCX or TXT file here, or click to browse (max 10 MB)The file is read in your browser and is never uploaded to our servers. Resume text (or paste it here) Job description (optional, recommended)

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Your score and detailed report will appear here.

What this checker looks for in a Data Science Intern resume

With no job description pasted, the checker compares your resume against the keyword profile below. Paste a real posting into the job description box for a tighter, posting-specific match. Keywords are matched as whole words or phrases, so use the wording employers use, and only list skills you can discuss in an interview.

Core keywords (expected in most postings for this role)pythonmachine learningstatisticssqldata analysispandasmodelregression

Strong differentiatorsscikit-learntensorflowpytorchdata visualizationfeature engineeringnlpjupytergit

Nice to have, depending on the employerrsparkawsdockertableaukaggle

The core and differentiator lists are our editorial summary of commonly listed skills for this role, not scraped data. Employers differ; the posting you apply to always wins.

How the score reads

The checker scores experience, keywords, impact, education, structure and format together. On its own scale, 85 or higher is labeled Excellent, 70 to 84 Good, 50 to 69 Fair and below 50 Needs work. This is our own rubric, not an industry standard, and real applicant tracking systems differ. See how the score is calculated.

Stronger bullets for this role

Start with an action verb such as Trained, Evaluated, Cleaned, Built, Deployed, Improved, then add the tool, the scale and the result. Examples below are illustrations of the pattern; replace the numbers with your own true figures.

Weak: Did a machine learning project.

Stronger (example): Trained a gradient-boosted model on 100,000 records and improved AUC from 0.71 to 0.78 against a logistic baseline.

Weak: Used Python for data.

Stronger (example): Cleaned and merged three datasets with pandas and built a reproducible Jupyter notebook for the analysis.

Weak: Took a statistics course.

Stronger (example): Ran an A/B test analysis and reported a 4% lift with a 95% confidence interval.

Format advice for Data Science Intern applications

Name the dataset size, model and metric for each project.
Show the baseline and the improvement.
Keep GitHub links to clean notebooks and readmes.
Group skills (languages, libraries, tools).

Common reasons these resumes score low

Models named but no metric.
Kaggle or course certificates listed with no outcome.
Skills listed without a project that uses them.
No evidence of working with messy data.

Questions about this role

Do I need a master's degree?

Requirements vary by posting. Read what each role asks and let the checker show how your resume reads against its keywords.

What belongs on a data science resume?

Projects with data size, method and metric, plus the tools used and a link to the code.

Related guides

Educational estimate, not a guarantee of interviews or hiring outcomes. Examples are illustrative.