Lead Fraud Data Scientist
Tasks
- Analyze user behavior
- Apply clustering and outlier detection
- Build and maintain KPI dashboards
- Collaborate with product engineering risk and operations
- Conduct network analysis
- Define fraud machine learning strategy
- Deploy ML models in cloud
- Deploy and monitor models
- Design and execute A/B tests
- Develop end-to-end fraud detection models
- Engineer features
- Establish ML best practices
- Integrate ML scores with rule engines
- Investigate emerging fraud patterns
- Mentor junior data scientists
- Perform data exploration
- Train and validate models
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWS | Algorithmic Fairness | Azure | B testing | CI/CD | CatBoost | Clustering | GCP | Imbalanced Data | Isolation Forest | K-Means | K-Nearest Neighbors | K-nearest | LIME | Lasso | LightGBM | Logistic Regression | Looker | MLOps | Machine Learning | Model Explainability | Nearest neighbors | Pandas | Python | Ridge | SHAP | SQL | Sampling techniques | Scikit-learn | Statistics | Tableau | Unsupervised Learning | XGBoost
Education
N/A
Roles
Data Scientist | Lead | Lead Data | Lead Data Scientist | Scientist
Regions
Countries
States
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