Lead Fraud Data Scientist
Tasks
- Apply clustering and outlier detection
- Build and maintain dashboards
- Build fraud detection models end to end
- Collaborate with product engineering risk and operations
- Conduct deep-dive fraud investigations
- Define long term machine learning strategy
- Deploy monitor and maintain models in cloud
- Design and execute A/B tests
- Detect lending fraud typologies
- Integrate ML scores with rule engines
- Mentor junior data scientists
- Monitor and maintain model performance
- Perform data exploration and feature engineering
- Perform network analysis
- Track KPIs fraud loss rate and false positive rate
- Train validate and deploy models
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWS | Algorithmic Fairness | Automated retraining | Azure | B testing | CI/CD | CatBoost | Fraud Detection | GCP | Graph Neural Networks | Graph analytics | Imbalanced Data | Isolation Forest | K-Means | K-Nearest Neighbors | K-nearest | LIME | Lasso | LightGBM | Logistic Regression | Looker | MLOps | Machine Learning | Model Deployment | Model Monitoring | Nearest neighbors | Neo4j | NetworkX | Neural Networks | Pandas | Python | Ridge | SHAP | SQL | Sampling techniques | Scikit-learn | Statistics | Tableau | Versioning | Vertex AI | XGBoost
Education
N/A
Roles
Data Scientist | Lead | Lead Data | Lead Data Scientist | Scientist
Regions
Countries
States
Cities
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