What Recruiters Look For
Data science is highly competitive. Your resume must show both technical depth and business impact.
Essential Sections
Technical Skills — Organize by category:
- Languages: Python, R, SQL, Scala
- ML/AI: TensorFlow, PyTorch, Scikit-learn, Hugging Face
- Data: Spark, Airflow, dbt, Kafka
- Cloud: AWS SageMaker, GCP Vertex AI
Projects (Critical) — Include 2-3 with:
- Problem statement and data size
- Methodology and algorithms
- Business impact with metrics
Example:
Built churn prediction model using XGBoost on 2M+ records, achieving 92% accuracy, reducing churn by 18%, saving ~$1.2M annually.
Show Business Impact
Weak: Built ML models for marketing team
Strong: Developed propensity model improving campaign ROI by 45%, generating $3.2M incremental revenue
Common Mistakes
- Listing every tool you ever touched
- No business impact — "Built a model" without results
- Ignoring soft skills
- Only academic projects — add Kaggle or personal projects
Use our Developer template optimized for technical roles.