Data Scientist CV Example

A data scientist CV should show the models you built, the metrics you used and the business impact you created, all backed by numbers. Saying "I know machine learning" is not enough; what matters is which problem you solved, with which data, on which metric and by how much you improved it.

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Sample Data Scientist CV

Sophie Bennett

Data Scientist

Manchester, UK

Summary

Data scientist with 5 years of experience; I have built end-to-end models for churn prediction, demand forecasting and recommendation systems. I am strong in Python, SQL and cloud-based MLOps tools, and I never consider a job finished until the model is in production.

Experience

Data Scientist · E-commerce technology company

Feb 2023 – Present
  • ›Developed a customer churn prediction model (gradient boosting), raising AUC from 0.71 to 0.84; reduced churn with targeted campaigns.
  • ›Moved the demand forecasting model onto a weekly automated retraining pipeline (Airflow + MLflow), greatly reducing manual workload.
  • ›Validated recommendation algorithm changes with A/B tests and delivered a measurable increase in add-to-basket rate.
  • ›Built self-service dashboards for model outputs to support data literacy across business units.

Data Analyst · Financial technology company

Jul 2020 – Jan 2023
  • ›Prepared inputs for a scoring model through feature engineering on loan application data with SQL and Python.
  • ›Automated monthly management reporting, cutting preparation time from days to hours.
  • ›Reduced the false-alarm rate of a rule-based fraud alerting system through analysis.

Skills

Python (pandas, scikit-learn)SQL & data modellingMachine learning (XGBoost, LightGBM)Deep learning (PyTorch) fundamentalsMLOps (MLflow, Airflow, Docker)A/B testing & experiment designStatistics & hypothesis testingData visualisation (Tableau/Power BI)

Education

BSc Industrial Engineering · University of Manchester (2015 – 2019)

MSc Data Science · University of Edinburgh (2019 – 2021)

What to Highlight in a Data Scientist CV

  • ✓For every project, build the problem → data → model → metric → business impact chain in a single sentence.
  • ✓State metrics explicitly: AUC, RMSE, precision/recall; "successful model" carries no information.
  • ✓Especially emphasise production experience (deployment, monitoring, retraining).
  • ✓Add your GitHub and, if you have one, your Kaggle profile link to the contact section so code quality is visible.
  • ✓Do not pad the tools list; write only the technologies you genuinely used in projects, as they will all be asked about at interview.

Technical Skills

  • •Python & scientific libraries
  • •SQL & data warehouse queries
  • •Supervised/unsupervised learning
  • •Feature engineering
  • •MLOps & model deployment
  • •Experiment design & A/B testing
  • •Statistical modelling
  • •Cloud platforms (AWS/GCP)

Soft Skills

  • •Translating business problems into models
  • •Explaining to non-technical stakeholders
  • •Analytical & critical thinking
  • •Curiosity & continuous learning
  • •Prioritisation & pragmatism

Common Mistakes

  • ✕Saying "I did machine learning projects" without naming the model and metric.
  • ✕Presenting course/tutorial projects with no business impact as real experience.
  • ✕Filling the skills list with dozens of tools you never used.
  • ✕Never describing production (deployment) experience; a model that stays in a notebook is an incomplete story.
  • ✕Not including a GitHub/portfolio link.

Frequently Asked Questions

Which metrics should a data scientist CV include?

A model metric suited to the problem (AUC, F1, RMSE, MAPE) and, where possible, a business metric (conversion uplift, cost reduction) together. "AUC 0.84" alone is good; "AUC 0.84 → lower campaign cost" is far stronger.

How do you show Kaggle and personal projects on a CV?

In a separate "Projects" section, written briefly with your competition ranking (e.g. top 5%), the method used and a link. If your work experience is thin this section can be moved up, but it does not replace real experience, so keep it balanced.

How do you frame a CV for the move from data analyst to data scientist?

Highlight model-adjacent work from your analyst experience (feature engineering, statistical analysis, forecasting) and back it with a master's, certificates and end-to-end personal ML projects. Describe the nature of your work rather than your job title.

How do you add LLM and generative AI experience to a CV?

Write it with a concrete use case: "I built a RAG-based document search prototype." Statements like "I use ChatGPT" add no value; specify prompt design, evaluation (eval) and integration experience.

Build your own Data Scientist CV in 5 minutes

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