- Overseeing the assessment and analysis of credit risk for new and existing loans, including the evaluation of financial capacity, credit history, and market conditions. - Developing and implementing credit scoring models and risk assessment tools to support decision-making processes. - Monitoring and reviewing credit exposures, identifying potential risks and taking proactive measures to mitigate them. - Managing the credit portfolio, ensuring diversification and adherence to the company's risk appetite. Conducting regular portfolio reviews and stress testing to assess the impact of adverse economic conditions on credit quality. - Preparing comprehensive credit risk reports for senior management and the board, providing insights into risk exposure, trends, and potential issues. - Analyzing and interpreting credit risk data to identify emerging risks and recommend appropriate actions. - Leveraging technology and data analytics to enhance credit risk management capabilities. - Staying abreast of industry developments and emerging technologies to ensure the company's credit risk practices remain competitive and effective. - Managing the credit risk department's budget and resources effectively, ensuring efficient use of funds and alignment with strategic priorities. - Leading the credit risk response during financial crises or adverse market conditions, ensuring timely and effective risk mitigation actions. - Developing and implementing contingency plans to address potential credit risk events.
- Bachelor's degree in Finance, Banking, Risk Management, or a related field (PRM, FRM or CFA designation preferred) - Strong analytical skills with proficiency in loan portfolio management, credit risk measurement and management. - Strong in using data wrangling tools (e.g., SQL, Power BI, Python, R, or Julia). - In-depth knowledge of unsupervised learning like clustering and dimensionality reduction. - In-depth knowledge of supervised learning models like Logistic Regression, CART, SVM, KNN, and ensemble models. - In-depth knowledge of credit risk principles, financial analysis, and regulatory requirements. - In-depth knowledge of IFRS 9 and Basel III requirements for calculating expected credit loss (ECL). - In-depth knowledge of statistics, machine learning and deep learning is a must. - In-depth knowledge and ability to build predictive scorecards (credit, fraud, collection, churn, propensity, etc) boosted with machine learning. - Ability to use data science tools like R and Python is a must and additionally with Julia or MATLAB/Octave is a plus. - Experience with building scoring models using XGBoost, LightGBM or Catboost is a plus. - Professional at using English is a must and ability to speak Korean is a plus.
Benefits will be shared in details for successful candidates
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