Manager – Credit Risk (Quant)

A Leading Financial Services Group is seeking a highly technical Manager – Credit Risk (Quant) to join its risk analytics team. In this quantitative role, you will be responsible for designing, building, and validating predictive credit risk models. Furthermore, you will develop statistical scoring engines, probability of default (PD) structures, and loss given default (LGD) frameworks. Consequently, this position is vital for driving algorithmic underwriting choices and maintaining robust portfolio risk mitigation models.

The Manager – Credit Risk (Quant) must integrate advanced statistical concepts with practical financial data architectures. Additionally, you will write complex scripts to scrub large datasets and deploy scalable quantitative risk pipelines into the core systems. Therefore, the company is looking for a data scientist with deep experience in predictive modeling and quantitative finance. If you are highly proficient in Python, R, and database schemas, this Manager – Credit Risk (Quant) role offers a premier platform for technical innovation.

Key Responsibilities

• Design, build, and optimize quantitative credit risk models, including scorecards, PD, LGD, and EAD metrics.
• Develop and maintain scalable data pipelines to process massive structured and unstructured financial data sets.
• Write clean, efficient, and well-documented code using Python, R, and advanced SQL architectures.
• Implement ETL/ELT processes to integrate risk data from diverse source systems into central platforms.
• Conduct regular model performance validation, back-testing, and sensitivity analysis to track predictability.
• Ensure all developed models comply with regulatory data governance, security, and cloud privacy rules.
• Collaborate with data architects and business units to integrate risk analytics into real-time decision systems.
• Troubleshoot and fix data pipeline bugs, optimize queries, and perform routine database tuning.
• Draft comprehensive technical model documentation to satisfy internal audit and regulatory requirements.
• Provide technical mentorship to junior risk quants and advocate for clean code principles within the team.

Requirements and Qualifications

• Master’s degree or PhD in Statistics, Quantitative Finance, Financial Engineering, Mathematics, or Data Science.
• 3 to 6 years of experience as a quantitative developer, credit risk modeler, or data engineer.
• Expert programming skills in Python, PySpark, PySQL, or SAS, with strong database schema expertise.
• Proven knowledge of automated data warehousing platforms, ETL workflows, and cloud storage systems.
• Familiarity with financial risk metrics, accounting standards, and Basel or IFRS 9 concepts.