Deskripsi pekerjaan
- Work with business unit teams (credit underwriting, modeling, product, and sales management) to identify and extract potentially valuable data from source systems in to report.
- Support business units in identifying and implementing the most appropriate analytical approach through business inteligence dashboard (PowerBI, Tableau, Qlik Sense).
- Work closely with Data Modeler as well as PIC for external partners (Private Credit Bureau, Data Aggregator) to create scoring model in credit risk area accross all products.
- Ensuring scoring model is reviewed regularly to identify room for model predictive power improvement.
- Support Risk Head on the digital transformation initiatives and stress testing portfolio scenario.
- Conduct model analysis for credit acquisition, early warning and monitoring, enterprise risk management, and other purposes.
- Ability to combine both knowlegde on the credit risk analytics & management (historical behaviour) and as well as utilizing the data sources to manage & mitigate the risk.
- At least a Bachelor鈥檚 or Master's Degree from top local or overseas universities with excellent GPA in Statistics, Mathematics, Computer Science, Computing, Economics or Finance.
- Minimum three years experience in credit risk with ever involved in credit risk modeling project.
- Experience and deep understanding of SQL, R, Python programming, and machine learning models.
- Have sound knowledge in credit risk principles and credit loss concept and best practice applied to lending business.
- Experience with data visualization tools (Tableau, Power BI, Looker, Qliksense)
- Experience with statistical analysis (hypothesis testing, probability, regressions, experimentation logic and biases, data modeling techniques and time series analysis).
- Care about doing an excellent job and exceeding expectations with the quality of what you do in fast paced environment.
- Solid oral and written Indonesia and English communication skills and able to communicate every policies and analysis to varied users.
