What you'll be responsible for:
· Develop data sources, anti-fraud model, application scorecard model, presale and cross-sale analytics.
· Liaise with Risk Data Architecture manager, Chief Technology Officer, or Head Of Project Management for acquiring relevant data for modeling purpose, including searching for external data sources, data crawling, etc
· Collaborate with application development team (in-house or outsource) for planning and the implementation of the models into the products
· Gathering feedback and plan for model improvements and better customer experience on a regular basis
You should possess the following requirements:
· Degree in Mathematics or Statistic, Qualitative Research, Data Analytics, Machine Learning
· More than 1 years in data analytics
· Strong experience developing retail credit risk modelling, preferably application scorecard development or anti-fraud model experience
· Experience in retail or SME risk assessment engine and anti-fraud engine
· Good understanding of banking and finance businesses
· Experience with at least two of: SAS, R, Python, SQL
· Experience with machine learning - random forest, GBDT, SVM, is a plus
· Fast learner to understand existing risk models and conduct customization to fit into local markets
· Experience in team management, strong communication skills and organizational coordination ability
· Equipped with good professional integrity, highly responsible for work, ability to undertake work pressure and challenges
- Can work independently with minimum supervision
- Enjoy to achived target with a tight schedule
- have experience in telco, finance, and insurance