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As a Quant Modeling Associate – Credit Risk in your Credit innovation team. You will be responsible for developing stress testing models to forecast credit losses and build quantitative frameworks to enhance credit rating methodologies for fixed income securities. You will contribute to the entire modeling lifecycle, including data extraction, statistical analysis, model development, evaluation, and implementation. You will prepare comprehensive documentation and engage with various stakeholders to address their queries regarding the model's methodology. Your expertise in econometrics and statistics will also support research initiatives and in-depth analysis of credit markets. This is an exciting opportunity to apply your expertise in econometrics and statistical analysis to develop robust models, enhance existing methodologies, and drive impactful solutions in credit risk modeling

Job responsibilities

Develop statistically robust models to forecast credit losses for fixed income investment portfolio. Enhance existing model methodologies to improve their effectiveness, incorporating insights from new research in the credit risk domain. Support the validation team by addressing their queries and ensuring that models meet regulatory requirements and are fit for use. Design and create a scalable platform for implementing these credit risk models, enhancing their performance while maintaining accuracy. Conduct periodic reviews to ensure model performance is as expected. Mentor and train junior team members and colleagues, sharing best modeling practices and domain knowledge. Collaborate with external stakeholders, including regulators, auditors, and industry groups, to discuss and refine model methodologies, assumptions, and results.

Required qualifications, capabilities, and skills

Proficiency in statistical modeling techniques, including multivariate regression, time series analysis, panel data analysis, logistic regression, and machine learning algorithms. Professional experience or deep interest in data analytics, artificial intelligence and data visualization tools/ techniques Problem solving skills to create statistically robust models   Candidate must be able to lead, multitask and thrive in a fast-paced environment managing multiple ad-hoc analytical requests and prioritize work accordingly. A strong academic background, with a minimum of a bachelor's degree in a technical or quantitative field such as Statistics, Economics, Finance or Mathematics. 

Preferred qualifications, capabilities, and skills

Knowledge of regulatory modeling (CECL / CCAR /IFRS9) preferred. Proficiency in advanced analytical languages such as Python, R (Preferred)