Head Of Data Science & Credit Risk
Tasks
- Apply causal inference approaches
- Build early warning systems
- Build near real time decisioning pipelines
- Build partnerships with external data providers and credit bureaus
- Build real time decisioning pipelines
- 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 customer value optimization
- Design and deploy machine learning models for fraud detection
- Design and deploy machine learning models for segmentation
- Develop approval strategies
- Develop credit risk policies
- Drive A B testing experimentation
- Establish MLOps standards for deployment monitoring versioning and performance tracking
- Lead portfolio risk analytics
- Model expected credit loss
- Optimize unit economics provisioning strategies and capital allocation
- Perform stress testing
- Recruit mentor and develop data scientists and risk analysts
- Set risk thresholds
- Translate analytical outputs into executive insights
Perks/Benefits
- Career progression
- Cloud native infrastructure
- Equity participation
- Experimentation driven culture
- Mission-driven impact
- Modern ML Stack
- Ownership and autonomy
Skills/Tech-stack
A/B | A/B Testing | B testing | BigQuery | Causal Inference | Cloud Data | Cloud Data Platforms | Credit Risk | Credit Scoring | Customer Segmentation | Customer Value | Customer Value Optimization | Data platforms | Decisioning systems | Early Warning | Early warning systems | Expected Credit Loss | Fraud Detection | GCP | MLOps | MLflow | Machine Learning | Model Monitoring | Model versioning | Performance Tracking | Portfolio Risk | Portfolio risk management | Risk Management | SQL | Statistical modeling | Stress Testing | Underwriting models | Value optimization
Education
N/A
Roles
Data Science | Head | Head of Credit | Head of Credit Risk | Head of Data | Head of Data Science
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