Lead Fraud Data Scientist
Tasks
- Analyze fraud patterns using clustering outlier detection and network analysis
- Build dashboards and visualize fraud metrics
- Define machine learning strategy for fraud team
- Deploy fraud models with high availability
- Design and execute A/B tests
- Develop fraud detection models end to end
- Develop lending fraud mitigation models
- Establish machine learning best practices
- Integrate machine learning scores with rule engines
- Mentor junior data scientists
- Perform data exploration and feature engineering
- Train validate and monitor machine learning models
Perks/Benefits
- Annual performance bonus
- Health, dental, and vision plans
- Hybrid work option
- Remote work environment
- Stock options grant
Skills/Tech-stack
A/B | A/B Testing | AWS | Algorithmic Fairness | Azure | B testing | CI/CD | CatBoost | Clustering | GCP | Graph Neural Networks | Imbalanced Data | 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 | Tableau | Vertex AI | XGBoost
Education
N/A
Roles
Data Scientist | Lead | Lead Data | Lead Data Scientist | Scientist
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