Data Scientist Resume Example
Data scientist resumes must bridge technical ML expertise and business value. This example shows how to present model performance (AUC, precision, recall), pipeline architecture, and dollar-value business outcomes that prove your work drives decisions.
# Maya Johnson **Data Scientist** | New York, NY | maya.johnson@email.com | (555) 567-8901 [linkedin.com/in/mayajohnson](https://linkedin.com/in/mayajohnson) | [github.com/mayajohnson](https://github.com/mayajohnson) ## Summary Data scientist with 5 years of experience building production ML systems. Expertise in NLP, recommendation systems, and A/B testing at scale. Combines statistical rigor with business acumen to deliver models that drive measurable revenue impact. ## Experience ### Senior Data Scientist **Netflix** | New York, NY | Feb 2022 - Present - Built content recommendation model increasing viewer engagement by 12%, adding an estimated $45M in annual retention value - Designed and deployed NLP pipeline for automated content tagging, reducing manual labeling effort by 80% - Developed A/B testing framework used by 20+ data scientists, standardizing experimentation across the org - Led cross-functional project with product and engineering to optimize search ranking, improving click-through rate by 18% ### Data Scientist **Spotify** | New York, NY | Aug 2020 - Jan 2022 - Built fraud detection model (0.96 AUC) identifying fraudulent streams, saving $3.2M annually - Developed user segmentation model using clustering algorithms, enabling personalized marketing campaigns - Created automated feature engineering pipeline reducing model development time by 40% - Presented quarterly ML insights to VP-level stakeholders, influencing product roadmap decisions ### Junior Data Analyst **Accenture** | New York, NY | Jun 2018 - Jul 2020 - Analyzed customer churn patterns for telecom client, identifying 5 key predictive features - Built Tableau dashboards tracking KPIs for 3 Fortune 500 clients - Automated weekly reporting pipeline using Python and SQL, saving 10 hours per week ## Skills **Languages:** Python, R, SQL, Scala **ML/AI:** TensorFlow, PyTorch, scikit-learn, XGBoost, Hugging Face, LangChain **Data:** Spark, Airflow, dbt, BigQuery, Snowflake, Kafka **Tools:** Jupyter, MLflow, Weights & Biases, Tableau, Git ## Education ### M.S. Data Science **Columbia University** | 2016 - 2018 ### B.S. Statistics **Cornell University** | 2012 - 2016
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Tips for Your Data Scientist Resume
- 1.Quantify model impact: "Fraud detection model (0.96 AUC) saved $3.2M annually in false positives"
- 2.List specific ML frameworks and tools — TensorFlow, PyTorch, scikit-learn, XGBoost
- 3.Include your data pipeline work: ETL, feature engineering, A/B testing infrastructure
- 4.Mention business outcomes, not just technical metrics — hiring managers care about revenue impact
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