Credit Risk Data Scientist, Monee (2027 Graduate)

Monee is a leading digital payments and financial services provider in Southeast Asia, with a growing presence in Latin America. Its mission is to better the lives of individuals and businesses in the region with financial services through technology.

Monee delivers a comprehensive range of digital financial products and services, including mobile wallets, payment processing, credit, banking, and insurtech. By leveraging technology and strategic partnerships, Monee aims to drive greater digital financial inclusion, convenience, and accessibility for individuals and small businesses across Southeast Asia and Latin America.

Monee is part of Sea Limited, a global technology company.

• Build and deploy credit risk models (statistical, ML, deep learning) that directly determine who gets access to credit, your models will impact millions of lending decisions across consumer and SME products.


• Engineer features from massive-scale datasets spanning Shopee's e-commerce ecosystem, behavioral signals, and third-party financial data, turning raw data into a structured format that enhances model performance.


• Explore and apply Generative AI and Large Language Models (LLMs) to credit assessment, enhancing underwriting efficiency, and optimizing risk management processes.


• Conduct continuous data mining to generate actionable insights, supporting business decision-making and problem-solving.


• Own the full model lifecycle: development, validation, deployment, and performance monitoring in production.

• Bachelor’s degree in Data Science, Artificial Intelligence, Machine Learning, Business Analytics, Information Technology, Finance, Economics, Statistics, Mathematics, or other related fields, expected to graduate by July 2027 and join us by Aug 2027.


• Strong understanding and hands-on experience with Machine Learning Models (Logistic Regression, Random Forest, Gradient Boosting Models - XGBoost / LightGBM etc.) 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 as 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.