Job Duties
-Utilize SQL to extract, clean, and process large datasets from various databases, ensuring data accuracy for risk analysis and reporting.
-Support the development and iteration of credit risk models and anti-fraud rules using machine learning algorithms such as XGBoost, GBDT, and Logistic Regression.
-Analyze financial and transactional data to identify potential risk trends, abnormal fluctuations, and fraud patterns to support decision-making.
-Perform feature engineering and data exploration to identify key risk indicators that improve model predictive performance.
-Monitor the performance of risk models and automated rules in production, assisting in troubleshooting and system stability checks.
-Collaborate with cross-functional teams, including Data Engineers and Product Managers, to translate data findings into actionable risk strategies.
Requirements
-Bachelor’s Degree or higher in a quantitative field such as Statistics, Mathematics, Computer Science, or Financial Engineering.
-Solid proficiency in SQL is a must; comfortable with complex queries and data manipulation in environments like MySQL, Hive, or Oracle.
-Proficient in Python for data analysis, with experience using libraries such as Pandas, Scikit-learn, or Matplotlib.
-Basic understanding or academic exposure to predictive modeling, including credit scorecards, classification, or regression analysis.
-Strong logical thinking and numerical reasoning skills, with a keen eye for detail and data integrity. Familiarity with statistical tools or other programming languages (e.g., R, SAS) is an advantage.
-For Junior level: Open to fresh graduates or candidates with 1–2 years of relevant experience; academic or internship exposure to predictive modeling (e.g., credit scorecards or regression analysis) is preferred.
-For Mid-level: 3–5 years of professional experience in Fintech, Banking, or Risk Control with a proven track record of deploying models into production
