Data Modelling & Customer Profiling:
- Build and maintain predictive models for customer segmentation and risk assessment.
- Develop clustering techniques to better understand customer behaviour patterns.
Credit Risk Analytics:
- Create scoring models for default risk fraud detection and creditworthiness evaluation.
- Develop risk modelling and stress testing frameworks.
Model Deployment & Monitoring:
- Work with tech teams to deploy models into production systems.
- Monitor model performance and adjust when needed.
Stakeholder Engagement & Communication:
- Build relationships with internal stakeholders customers and external partners.
Compliance & Data Governance:
- Ensure models meet internal policies and regulatory requirements.
- Maintain data accuracy ethics and security standard.
- ./. in Data Science Statistics Computer Science or related field.
- Professional certifications in data analytics or machine learning (preferred).
- Minimum 4 years in data science or risk analytics roles.
- Experience in financial services particularly lending is strongly preferred.
Technical:
- Predictive modelling & machine learning.
- Credit risk analysis & scoring.
- SQL Python.
- Risk modelling and stress testing.
- Experience with cloud platforms (AWS Azure).
Behavioral:
- Analytical and curious mindset.
- Effective communication and storytelling skills.
- Strong stakeholder management.
- Team collaboration.
- Accountability and ownership.
- Continuous learning & improvement mindset.