Data Scientist Resume Guide — Skills and Projects That Stand Out

Data science hiring managers look for three things: strong fundamentals, real project experience, and business impact. Show all three.

Quantify model performance: 'Built a churn prediction model achieving 91% AUC, identifying at-risk customers 3 weeks earlier.'

Show business outcomes, not just technical ones: 'Improved customer retention by 12% through an ML-driven retention campaign.'

List your stack: Python, R, SQL, PyTorch, TensorFlow, scikit-learn, Spark, Airflow, dbt, Docker, MLflow, and relevant cloud services.

Projects matter more than coursework. Describe 2-3 end-to-end projects: the problem, data, approach, model, and result.

For GenAI roles, highlight RAG systems, LLM fine-tuning, vector databases, prompt engineering, and agent frameworks.

Include data engineering skills if you have them: ETL pipelines, data warehousing, feature stores, and data quality monitoring.

Keep your skills section categorized (Programming, ML, Data Engineering, Visualization) so it's easy to scan.