Data Science (Risk) - Monee (Campus Recruitment 2026)

Job DescriptionDevelop credit risk models to accurately assess user credit worthiness, supporting the expansion of Retail Finance and SME Finance businesses.Identify fraudulent behaviors from large-scale user data and build models to detect anomalies and mitigate fraud risks.Develop and maintain a feature pool consisting of both Shopee ecosystem data and third-party data, extracting valuable insights from massive datasets. Utilize statistical methods and machine learning techniques to transform raw data into a structured format that enhances model performance.Explore and apply deep learning and Generative AI in credit assessment to improve risk detection, enhance underwriting efficiency, and optimize risk management processes.Build and maintain graph databases, and explore the applications of graph-based models in risk management.Conduct continuous data mining to generate actionable insights, supporting business decision-making and problem-solving.RequirementsOpen to fresh graduates, graduating between January 2025 and August 2026.Bachelor’s degree in Machine Learning, Business Analytics, Information Technology, Finance, Economics, Statistics, Mathematics, or other related fields.Strong understanding and hands-on experience with machine learning models (e.g., boosting trees, regression models) and feature engineering techniques.Solid statistical knowledge and strong data analysis abilities, with the capability to identify problems from data and develop data-driven solutions.Proficiency in SQL and Python is mandatory, with experience in PySpark being a plus.Strong passion and curiosity for credit business, risk management, and data analytics.Experience in network analysis, search and recommendation systems, or other machine learning fields is a plus.