Job Description
• 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 take over and so on
• Analyse and conduct feature engineering for massive data such as customer profiling, e-commerce transaction, 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 need and insight into machine learning models
• Research model methodology and data mining techniques to improve model performance

Requirements
• 1-5 years relevant credit or anti-fraud model development experience
•... Experienced with data mining and feature engineering from massive raw data especially the alternative credit data
• Solid understanding and hands on experience of machine learning models such as boosting trees, regression models and good sense in feature engineering
• Good coding skill using SQL, Spark and Python
• Eager to learn new things and has passion in work
• Take responsibility, team oriented, result oriented, customer oriented and self driven
• Experience in network analysis, search and recommendation system and other machine learning field is a plus
Singapore

Salary Criteria