Data Science · Graduate Scheme

How to write a data science student CV for a graduate scheme

Graduate schemes are competitive and heavily screened — recruiters want evidence of competencies, commercial awareness and measurable impact. Here's how to do it as a data science student.

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What matters for a graduate scheme

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Evidence competencies

Map your examples to the scheme's competencies: teamwork, leadership, problem-solving.

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Quantify everything

Numbers signal impact — budgets, users, results, team sizes and improvements.

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Flawless & ATS-safe

Large employers screen at scale, so keep it clean, consistent and error-free.

Data Science specifics

Data and analytics recruiters look for projects, statistical and programming skills, and the ability to turn data into insight. 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.”

Frequently asked questions

What do graduate scheme recruiters look for on a CV?

Evidence of their target competencies, quantified achievements, and a tailored, error-free one-page CV that passes ATS screening.

Should I tailor my CV to each graduate scheme?

Yes — mirror each scheme's competencies and language. Careero makes it fast to adapt your CV per application.

What projects should a data science student CV include?

Two to four end-to-end projects that show the full pipeline — sourcing data, analysis, and a clear, communicated insight.

Data Science CV for other goals

Build your data science CV for a graduate scheme

Answer a few guided questions and download a tailored, ATS-friendly CV.

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