Quick takeaway: show model quality and business outcomes, not only algorithms.
What hiring teams check
Data science resumes should connect technical depth to business value. Recruiters look for experimentation quality, model performance, and real product impact.
Data scientist summary example
Data Scientist with 4 years of experience building predictive models and experimentation frameworks for B2B SaaS. Improved lead scoring precision by 27% and increased conversion by 8% through feature engineering and model monitoring.
Model impact bullet samples
Example bullet
“Built churn prediction pipeline in Python and SQL, improving recall from 0.54 to 0.79 and enabling retention team interventions that reduced monthly churn by 6%.”
Skills section
Core: experimentation, model evaluation, feature engineering
Programming: Python, SQL, R
Tools: scikit-learn, TensorFlow, dbt, BigQuery, Tableau
Final checks before applying
Validate your wording in the ATS checker and ensure metric statements are clear and auditable.