Data Science (Risk) - SeaMoney Credit

Work on risk model development for retail and SME finance products such as consumer lending, personal finance, small and medium-sized enterprises loan and so on.

Build models and tools for credit and fraud risk identification in various aspects. For example, credit risk modelling, income estimation, customer information verification, anti cash-out, non-starter detection, account takeover, and so on.

Analyse and conduct feature engineering for massive data such as customer profiling, e-commerce transactions, and so on and deploy the feature pipeline.

Using graph mining, time series data modelling, graph & item embedding techniques to extract information from raw data.

Collaborate closely with the risk policy and business team. Translate business needs and insights into machine learning models.

Research model methodology and data mining techniques to improve model performance.

• Bachelor's degree in Machine Learning, Business Analytics, Information Technology, Finance, Economics, Statistics, Mathematics, or a related field. Master's and PhD degrees are preferred.
• 1-5 years relevant credit or anti-fraud model development experience.
• Experienced with data mining and feature engineering from massive raw data, especially alternative credit data.
• Solid understanding and hands-on experience of machine learning models such as boosting trees, regression models, and a good sense in feature engineering.
• Good coding skills using SQL, Spark, and Python.
• Eager to learn new things and has passion for work.
• Take responsibility, team-oriented, result-oriented, customer-oriented, and self-driven.
• Experience in network analysis, search and recommendation systems, and other machine learning fields is a plus.