Head Of Data Science & Credit Risk
Tasks
- Build customer value optimization systems
- Build early warning systems for deterioration
- Define end to end data science and credit risk strategy
- Design machine learning models for credit scoring
- Design machine learning models for fraud detection
- Design machine learning models for segmentation
- Develop near real time decisioning pipelines
- Develop real time decisioning pipelines
- Drive experimentation with A/B testing
- Drive experimentation with causal inference
- Establish MLOps standards
- Improve credit approval strategies
- Improve credit risk policies
- Model expected credit loss
- Monitor model performance in production
- Optimize Unit Economics
- Optimize provisioning strategies
- Partner with data providers and credit bureaus
- Perform stress testing
- Recruit mentor and develop team
- Run portfolio risk analytics
- Set risk thresholds for underwriting
- Support capital allocation decisions
- Translate analytics into executive insights
Perks/Benefits
- Access to modern ML stack
- Career progression
- Direct impact on financial inclusion
- Equity participation
- Fast paced mission driven environment
- Opportunity for autonomy and ownership
Skills/Tech-stack
A/B | A/B Testing | B testing | BigQuery | Causal Inference | Credit Risk | Credit Scoring | Customer Segmentation | Data Processing | Early Warning | Early warning systems | Expected Credit Loss | Fraud Detection | GCP | MLOps | MLflow | Machine Learning | Near-real-time | Near-real-time systems | Real Time | Real-time Data | Real-time Data Processing | Real-time Systems | SQL | Statistical modeling | Stress Testing | Time Systems | Underwriting
Education
N/A
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