CV by Field · Data Science
How to write a data science student CV
Data and analytics recruiters look for projects, statistical and programming skills, and the ability to turn data into insight.
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What matters most on a data science CV
Data projects
End-to-end projects — question, data, method, insight — are your strongest evidence.
Tools & languages
Python, R, SQL, pandas and visualisation libraries are core, screenable skills.
Statistics & ML
Name the methods you've genuinely applied — regression, classification, clustering.
Clear, ATS-safe layout
Keep it clean so both humans and screening software parse it.
How to describe a data project
Frame it as a question and an insight: “Analysed 10k survey responses in Python to identify three churn drivers; visualised findings in a dashboard used by the society committee.”
Writing your data science CV for a specific goal?
For an internship
Internship recruiters know you're early-career — they're screening for relevant skills, enthusiasm and evidence you can contribute quickly.
Learn more →For a scholarship
A scholarship CV rewards merit and potential — committees look for academic strength, leadership and community impact, not job history.
Learn more →For a part-time job
For part-time roles, employers care about reliability, attitude and availability far more than a polished career history.
Learn more →For your first job
Your first graduate-level job CV should convert your degree, projects and any experience into evidence you can do the role from day one.
Learn more →For a graduate scheme
Graduate schemes are competitive and heavily screened — recruiters want evidence of competencies, commercial awareness and measurable impact.
Learn more →Frequently asked questions
What projects should a data science student CV include?
Is Careero free for students?
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