Credit Risk Modeller / Analyst at Hello Group

We are seeking a highly analytical and detail-oriented Credit Risk Modeller to join our Lending Team. The successful candidate will be responsible for building, validating, and maintaining credit risk models used for decision-making, calibrating, regulatory compliance, and strategic risk assessment. This role is ideal for a technically skilled individual who is passionate about data, modelling, and developing insights that drive business impact.

Duties and Responsibilities    
Develop, implement, and maintain models for credit risk measurement related to; 

• Merchant Advances
• Consumer Advances
• Create and refine credit scoring models to support lending decisions and customer lifecycle strategies.
• Conduct model performance monitoring, diagnostics, and ongoing validations to ensure accuracy and stability.
• Collaborate with Data Engineers and Business Analysts to ensure data quality and integrity for model inputs.
• Produce clear, well-documented model development and validation reports for governance and audit purposes.
• Contribute to stress testing and scenario analysis for regulatory or internal capital planning.
• Stay current with regulatory changes, data science trends, and modelling techniques.
• Engage with internal committees, governance teams, and stakeholders to explain model outcomes and support decision-making.
• Support automation and integration of models into operational systems using appropriate tools and programming languages.

Minimum Requirements    

• A degree in Statistics, Actuarial Science, Mathematics, Data Science, Quantitative Risk Management, or a related field.
• A postgraduate degree (MSc/PhD) is advantageous.
• 3–7 years’ experience in credit risk modelling, financial modelling, or data analytics within financial services, fintech, or consulting.
• Experience building credit models based on data-driven insights.
• Solid understanding of credit lifecycle, risk metrics, and regulatory frameworks.
• Experience working in a fintech or data-driven environment.
• Hands-on experience with statistical programming (e.g., R, Python, SAS) and data manipulation (SQL).
• Familiarity with modern machine learning approaches is beneficial, though not essential.

Deadline:4th July,2025