Head Of Data Science & Credit Risk

Tasks

• Build early warning systems for portfolio deterioration
• Build real time decisioning pipelines
• Build underwriting models and decisioning systems
• Define end to end data science and credit risk strategy
• Design deploy machine learning models for credit scoring
• Design deploy machine learning models for fraud detection
• Develop improve credit risk policies and approval strategies
• Drive experimentation culture A B testing
• Establish MLOps standards for model deployment
• Implement causal inference approaches
• Lead portfolio risk analytics stress testing
• Manage credit policy risk thresholds and appetite
• Model expected credit loss
• Monitor versioning and performance tracking of models
• Optimize unit economics provisioning strategies capital allocation
• Recruit mentor and develop data science and risk analysts

Perks/Benefits

• Access to modern ML stack
• Equity participation
• Fast-paced environment
• High impact mission oriented work
• Opportunity for career progression
• Ownership and autonomy

Skills/Tech-stack

A/B | A/B Testing | B testing | BigQuery | Causal Inference | Credit Risk | Credit Scoring | Customer Value | Customer Value Optimization | Data Analysis | Deployment Pipelines | Early Warning | Early warning systems | Expected Credit Loss | Fraud Detection | Google Cloud | Large Scale Data | Large-scale | Large-scale data analysis | MLOps | MLflow | Machine Learning | Model Monitoring | Model versioning | Risk Modeling | SQL | Stress Testing | Underwriting | Value optimization