Head Of Data Science & Credit Risk
Tasks
- Build and scale partnerships with data providers
- Build decisioning pipelines for credit underwriting
- Build early warning systems for portfolio deterioration
- Define end to end data science and credit risk strategy
- Design and deploy machine learning models for credit scoring
- Design and deploy machine learning models for fraud detection
- Develop A/B testing frameworks
- Develop and improve credit risk policies
- Establish risk thresholds and approval strategies
- Lead experimentation with AB testing
- Monitor model performance in production
- Perform portfolio risk analytics
- Recruit, mentor, and develop data science team
- Run stress testing and expected credit loss modeling
- Set up MLOps standards for model deployment
- Translate analytics into executive insights
Perks/Benefits
- Autonomy
- Career progression
- Cloud native infrastructure
- Equity participation
- Experimentation culture
- Financial inclusion impact
- Innovation
- Modern ML Stack
- Ownership
- Remote N/A
Skills/Tech-stack
A/B | A/B Testing | B testing | BigQuery | Causal Inference | Credit Risk | Credit Scoring | Early Warning | Early warning systems | Expected Credit Loss | Fraud Detection | GCP | MLOps | MLflow | Machine Learning | Risk Modeling | SQL | Statistical modeling | Stress Testing | Underwriting
Education
N/A
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