Senior Executive/Assistant Manager - Risk Management (Credit Decision Science)

If you are looking to excel and make a difference, take a closer look at us…

At the intersection of bold business strategy and AI-driven analytics lies our Credit Decision Science team. We are looking for visionaries to architect the intelligence that fuels the Bank's strategic expansion. In this role, you won't just support the business-you will leverage multi-dimensional modeling to navigate new frontiers and scale aggressively. Join us to build the robust frameworks that don't just stay within risk appetites, but actively create the path for the Bank's future success.

Responsibilities

• Architect the Future of Credit Intelligence: Lead the end-to-end re-engineering of the Bank's predictive engine, deploying sophisticated multi-dimensional and AI-driven models that move beyond traditional risk management into the realm of strategic business optimization.

• Drive Multi-Billion Dollar Strategy: Act as a high-level consultant to Business Unit leaders, translating complex algorithmic outputs into actionable commercial strategies that directly capture new market opportunities and fuel the Bank's growth momentum.

• Innovation Without Boundaries:Mastermind the transition from legacy frameworks to cutting-edge Decision Science, utilizing AI to solve real-world financial challenges and creating intellectual legacy within a forward-thinking banking ecosystem.

• Direct Impact on Value Creation: Ownership of the model lifecycle where your technical precision doesn't just mitigate risk, it scales financing capacity and expands business volume, empowering the Bank to capture new markets and drive sustainable growth.

• Collaborative Leadership & Influence: Work at the intersection of data and vision, collaborating with cross-functional experts to ensure our models are not just statistically robust, but are the primary catalysts for winning in a competitive and volatile financial landscape.

Skills&ExperienceWeAreLookingFor:

• Quantitative Foundation: A strong academic background in a quantitative field (e.g. Data Science, Statistics, or Financial Engineering) with 3-5 years of experience in credit risk modeling or advanced analytics.

• Computational Fluency: Hands-on expertise in Python and SAS, with a proven ability to engineer and transform large-scale datasets into high-performance model inputs, and a passion for deploying AI/Machine Learning frameworks within a regulatory environment.

• Multi-Dimensional Insight: Experience in developing or recalibrating credit models, with a sophisticated understanding of how multi-dimensional variables impact the model's predictive power and long-term stability.