Job Description
  • Work on risk model development for retail and SME finance products such as consumer lending, personal finance, SME 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
  • 3-6 years relevant credit or antifraud 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

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