How to Write a CV That Gets You Hired as a Data Scientist

Writing a CV for a data science role requires a different approach than traditional CVs. Here's our comprehensive guide based on screening over 10,000 data science applications.

The Perfect Structure

1. Professional Summary (3-4 lines) Lead with impact. Don't list technologies — show results.

Bad: "Experienced data scientist with Python skills." Good: "Data Scientist with 5 years' experience delivering ML models that generated £2.3M in additional revenue for FTSE 100 retailers."

2. Technical Skills Section Organise by category: - **Languages:** Python, R, SQL, Scala - **ML/AI:** TensorFlow, PyTorch, scikit-learn, XGBoost - **Data:** Spark, Airflow, dbt, BigQuery - **Visualisation:** Tableau, Power BI, Matplotlib

3. Experience (Impact-First) Use the STAR method but lead with the result:

"Reduced customer churn by 23% (£1.2M annual saving) by developing a gradient-boosted prediction model using 18 months of behavioural data."

4. Projects & Portfolio Include 2-3 relevant projects with: - Problem statement - Approach and methodology - Technologies used - Measurable outcomes - Link to GitHub/notebook

Keywords That Matter ATS systems scan for specific terms. Include these naturally: - Machine Learning, Deep Learning, NLP - A/B Testing, Statistical Analysis - Data Pipeline, ETL, Feature Engineering - Model Deployment, MLOps, CI/CD

Common Mistakes 1. **Too long** — Keep it to 2 pages maximum 2. **No metrics** — Quantify everything you can 3. **Generic** — Tailor for each application 4. **Missing GitHub** — Always include your portfolio link

Final Tip Have your CV reviewed by a specialist recruiter (like us!). We see thousands of CVs and know exactly what hiring managers prioritise.