Head Of Data Science & Credit Risk

Tasks

• Apply causal inference approaches
• Build customer segmentation models
• Build early warning systems
• Coordinate experimentation and data driven optimization
• Define approval strategies and risk thresholds
• Define end to end data science strategy
• Design and deploy machine learning credit scoring models
• Design fraud detection models
• Develop credit risk policies
• Develop real time decisioning pipelines
• Drive A/B testing frameworks
• Establish MLOps standards for model deployment
• Lead underwriting models and decisioning systems
• Model expected credit loss
• Monitor model performance in production
• Optimize customer value
• Optimize unit economics and provisioning strategies
• Recruit and mentor data science and risk teams
• Run portfolio stress testing
• Support capital allocation

Perks/Benefits

• Access to modern ML stack
• Cloud native infrastructure
• Equity participation
• Experimentation focused culture
• Fast paced mission driven environment
• Opportunity to build capabilities
• Ownership and autonomy
• Strong career progression

Skills/Tech-stack

A/B | A/B Testing | B testing | BigQuery | Causal Inference | Credit Risk | Credit Scoring | Customer Segmentation | Data Pipelines | Decisioning systems | Early Warning | Early warning systems | Expected Credit Loss | Fraud Detection | GCP | MLOps | MLflow | Machine Learning | Model Monitoring | SQL | Stress Testing | Underwriting