At TEB, we are looking for a Quantitative Finance and Risk Metrics Manager / Assistant Manager to join our ALM Modelling team.

This critical role is responsible for supporting the development of quantitative models used to analyze the Bank’s financial risks and customer behaviors, contributing to data-driven decision making across the organization.

Key Responsibilities:

• Develop and Maintain ALM Frameworks: Build, maintain, and improve quantitative frameworks, mathematical models, and periodic production plans to manage interest rate and liquidity risks in the banking book, adapting to changing regulatory environments.
• Model Customer Behavior: Research and develop innovative methodologies to analyze customer behavior and optimize cost estimation models for internal transfer mechanisms from interest rate and liquidity risk perspectives.
• Manage End-to-End Model Validation: Take full ownership of the model lifecycle by developing robust models, writing comprehensive code and documentation, and presenting them to Local Risk and BNP Global Risk for formal validation and approval.
• Execute Projects & Team Collaboration: Manage and execute ALM and Treasury projects from planning to end-to-end delivery under tight deadlines, while assisting teams with efficiency-enhancing initiatives and fulfilling regulatory requirements.

Qualifications:

• University graduate (preferably in Statistics, Econometrics, Mathematics, Engineering, or Finance).
• Minimum 5 years (for Manager) or min. 2 years (for Assistant Manager) of hands-on experience in financial, statistical, or econometric modeling within the banking or financial services sector.
• Excellent command of English (both written and verbal), with the ability to confidently present, discuss, and defend complex quantitative models in international meetings and drive business development initiatives.
• Knowledgeable about interest rate, pricing, prepayment, and replicating portfolio models.
• Strong technical knowledge in modeling, with a solid theoretical and mathematical foundation in both Machine Learning and Statistical Modeling, along with proficiency in Python and SAS.