Lead Fraud Data Scientist
Tasks
- Apply sampling for imbalanced data
- Build dashboards for KPIs
- Build features for fraud detection
- Conduct emerging fraud pattern analysis
- Define fraud machine learning strategy
- Deploy monitor maintain models in cloud
- Design execute A B tests
- Detect lending and credit fraud
- Establish machine learning best practices
- Integrate machine learning scores with rule engines
- Mentor junior data scientists
- Monitor fraud models
- Own fraud model lifecycle
- Perform clustering and outlier detection
- Perform data exploration
- Run network analysis
- Track fraud loss rate false positive rate model performance
- Train validate deploy fraud models
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | AWS | Algorithmic Fairness | Azure | B testing | Bureau data | CI/CD | CatBoost | Clustering | Credit Bureau Data | Credit bureau | ECOA | FCRA | GCP | Graph Neural Networks | Graph analytics | Isolation Forest | K-Means | K-Nearest Neighbors | K-nearest | LIME | Lasso | LightGBM | Logistic Regression | Looker | MLOps | Machine Learning | Model Explainability | Model Monitoring | Nearest neighbors | Neo4j | NetworkX | Neural Networks | Outlier Detection | Pandas | Python | Ridge | SHAP | SQL | Sampling techniques | Scikit-learn | Statistics | Tableau | Vertex AI | XGBoost
Education
N/A
Roles
Data Scientist | Lead | Lead Data | Lead Data Scientist | Scientist
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