CV Review for Data Scientists
Data science hiring is unusually competitive. Recruiters sift through CVs where half the candidates have the same degrees and tools. What differentiates you is how clearly you articulate business impact — not the list of models you've run.
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What recruiters are actually looking for
Hiring managers in data science look for evidence of the full cycle: problem framing, model development, deployment, and business outcome. They also check for tool fluency (Python, SQL, cloud platforms) and communication ability. Academic CVs heavy on theory but light on applied outcomes are a common rejection reason.
How to make your CV stand out
Specific advice for your field — not generic tips that apply to everyone.
Describe business outcomes, not model types
Avoid 'trained an XGBoost classifier'. Write 'built a churn prediction model that reduced customer attrition by 12%, saving ~£200K annually'. The model architecture is secondary to the result it produced.
Separate tools from skills clearly
Hiring managers need to quickly scan for Python, SQL, TensorFlow, dbt, Spark, or whatever is in their stack. Use a dedicated Tools/Tech section. Don't just bury these in paragraph form — make them parseable at a glance.
Be specific about your data engineering depth
Are you a modeller, or can you also build pipelines? The gap between 'can use pandas' and 'owns the entire data platform' is enormous. Be explicit about where you sit — vagueness leads to mismatched interviews.
Showcase a signature project
A detailed project or published paper — with dataset scale, methods, and outcomes — gives reviewers something to anchor on. Even a Kaggle competition with a strong placement is worth mentioning if you're earlier in your career.
Common mistakes that get CVs rejected
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Listing ML frameworks without showing real deployment experience
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Focusing on methodologies rather than business impact
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No evidence of SQL or data engineering skills
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GitHub/Kaggle profile missing or not linked
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Overly academic tone unsuited for industry roles
How Bluntly reviews your CV
Not a keyword scanner. Not a template checker. A real analysis that reads your CV the way a hiring manager would.
ATS compatibility check
See exactly how applicant tracking systems parse your CV — including what gets lost or misread before a human ever sees it.
Role-specific feedback
Bluntly knows what hiring managers in your sector care about. The feedback is calibrated to your role, level, and target market.
Actionable improvements
Every suggestion tells you exactly what to change and why. No vague advice — specific rewrites and additions that make the difference.
Find out what's holding your CV back
Upload your CV now and get honest, role-specific feedback in under 2 minutes.
Get your data science CV reviewedFirst analysis free · €0.99 after · No card needed