Risk Analytics Manager

Job Description

The Manager – Credit Risk Modeling & Scorecard Development will be responsible for the end-to-end development of credit risk scorecards. This is a hands-on role, requiring strong technical and statistical modeling expertise, with a focus on building predictive models using advanced data analytics tools. The role involves working with large-scale datasets, applying statistical techniques, and deploying models that support credit decisioning and portfolio management strategies.

The ideal candidate will be passionate about modeling, experienced in handling real-world noisy data, and able to translate data-driven insights into actionable credit risk tools.

Responsibilities

• Develop and maintain scorecards (application and behavior) to support risk-based strategies across the customer lifecycle.
• Leverage advanced analytics and machine learning to improve predictiveness and business outcomes from risk models.
• Design and implement data-driven credit strategies that balance risk, profitability, and customer experience.
• Perform data extraction, preparation, and feature engineering using large structured datasets from internal and external sources.
• Apply statistical techniques including logistic regression, decision trees, and machine learning algorithms to build robust, interpretable models.
• Conduct model performance evaluation, including KS, Gini, PSI, stability, lift analysis, and backtesting to ensure model accuracy and consistency.
• Document the entire model development lifecycle (MDLC) in compliance with internal model governance and regulatory requirements (e.g., IFRS9, Basel).
• Collaborate with risk policy, credit underwriting, and collections teams to ensure models are effectively translated into business strategies.
• Provide analytical support in model calibration, cut-off analysis, stress testing, and scenario analysis.
• Participate in internal and external model validation exercises, audits, and regulatory reviews by preparing detailed documentation and responses.
• Continuously enhance models by incorporating alternative data, new modeling techniques, and automation tools.
• Performs ad hoc analyses as requested by the senior management.
  
  

• Bachelors or Master’s degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field.
• 5–10 years of hands-on experience in credit risk modeling, with a strong focus on scorecard development in banking or consumer lending.
• Strong programming skills in SAS (preferred), Python, R, and SQL for model building, data preparation, and validation.
• Deep understanding of statistical modeling techniques such as logistic regression, WOE/IV transformation, scorecard binning, and variable selection.
• Experience in developing models for retail lending products such as personal loans, credit cards, auto loans, or mortgages.
• Familiarity with model governance processes, documentation standards, and regulatory requirements (e.g., Basel, IFRS9, CB guidelines).
• Ability to work independently on data sourcing, feature creation, model training, and performance evaluation.
• Experience working with data visualization and reporting tools (e.g., Power BI, Tableau) is a plus.
• Strong communication and documentation skills to explain model logic to stakeholders, reviewers, and non-technical teams.