Develop and maintain MIS reports to monitor key portfolio performance and advise management on risk profiles.
Analyse portfolio performance, identify trends & drivers, draw insights and optimize the underwriting policies Credit
Design and implement champion challenger mechanism to optimise the underwriting.
Participate in credit portfolio stress testing on a regular basis to fulfill both internal and regulatory requirements.
Lead delivery of credit risk system projects and/or tactical system changes.
Administer credit rules, scorecards, limit assignments in retail and SME credit underwriting system(s), including the following: Administer risk data mart and conduct associated problem-solving.
Communicate issues/improvements relating to credit risk systems and data inputs/outputs, and act as primary liaison for vendor engagement, vendor management and onboarding.
Work closely with business team, dev, and other functions as necessary.
Requirements
Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field
min 2 years of hands-on experience in statistical modeling, machine learning, data science, or credit risk analytics roles
Strong proficiency in SQL — ability to write complex queries, perform data exploration, and build ETL logic for modeling datasets
Strong proficiency in Python — pandas, scikit-learn, and related ML libraries for model training and evaluation
Solid understanding of traditional ML methods: logistic regression, gradient boosting (XGBoost, LightGBM), random forests, ensemble methods
