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Data Scientist CV Template & Example
A data scientist CV has to prove your models drive real business decisions, not just sit in a notebook. Recruiters want the algorithms and languages you use, the lift or revenue your models produced, and evidence you can communicate findings to non-technical stakeholders.
Data Scientist CV professional summary example
Open with two or three lines covering what you do, how long you have done it, and one measurable result.
Data scientist with an MSc in Statistics and 5 years building production ML models, including a churn model that retained £2.3M in annual recurring revenue.
Core skills for a Data Scientist CV
The skills worth naming in your skills section. Different from the ATS keywords below, which are the terms a parser scans for.
- Python (pandas / scikit-learn)
- SQL & data wrangling
- Machine learning & A/B testing
- Statistical modelling
- Data visualization (Tableau / Plotly)
- TensorFlow / PyTorch
How to write a standout Data Scientist CV
Three things hiring managers and applicant tracking systems look for — get these right and you clear the first screen.
Translate models into business impact
State the outcome in money or percentage: 'churn model retained £2.3M ARR'. Hiring managers care about decisions driven, not just AUC scores.
Show the full pipeline
Mention data sourcing, cleaning, modelling, and deployment. Evidence you ship models to production, not just prototype, sets you apart.
List languages and libraries precisely
ATS scans for 'Python', 'SQL', 'scikit-learn', 'TensorFlow'. Match the stack in the posting and skip vague 'data analysis'.
ATS keywords for Data Scientist CVs
Applicant tracking systems scan for role-specific terms before a human reads your CV. Weave the ones that genuinely apply to you into your summary, skills, and bullets.
Data Scientist CV bullet point examples
Achievement-led bullets that show impact with numbers — concrete beats vague. Adapt them to your own results.
- Built a gradient-boosted churn model (AUC 0.89) that flagged at-risk accounts, enabling retention plays that saved £2.3M in annual recurring revenue.
- Designed and analyzed 40+ A/B tests on the checkout funnel, lifting conversion 12% and adding an estimated £800K in yearly revenue.
- Productionized a demand-forecasting model in Python that cut inventory holding costs by 18% across 14 warehouses.
Frequently asked questions
Common questions about writing a Data Scientist CV.
What should a data scientist put on a CV?
Models you shipped and their business impact, your languages and ML libraries, statistical methods, deployment experience, and degrees or certifications in stats or ML.
Do I need a portfolio or GitHub link?
Yes, when possible. A GitHub or Kaggle profile lets reviewers verify your code quality and is increasingly expected for data science roles.
How technical should the metrics be?
Pair model metrics like AUC or RMSE with business outcomes such as revenue retained or cost cut, so both technical and hiring-manager readers are convinced.
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