Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 2.5


I have 2.5 years experience in  Risk Data Science.Here is some projects that I apply in this field.
1.Development and monitoring of the predictive risk models such as the regulatory models (PD, EAD, LGD).
2.Collecting the data required for the development of statistical
models. 
3.Default prediction models: Developing statistical models to predict the likelihood of loan default or other forms of credit risk based on borrower characteristics and historical loan data.
4.Credit scoring models: Developing models to evaluate the creditworthiness of loan applicants or borrowers based on factors such as income, employment history, and credit history.
5.Develeopment and validation of IFRS PD model 

As a Technology:
Statistical analysis software: Software such as Python or Dataiku is used to develop, test, and validate statistical models for credit risk assessment.

Data visualization tools: Tools like Tableau is used to visualize and explore large data sets to identify patterns and relationships related to credit risk.

Database management systems: Systems like Oracle or SQL Server are used to store, manage, and analyze large data sets for credit risk assessments.

Machine learning libraries: Libraries such as scikit-learn or TensorFlow are used to develop and implement machine learning models for credit risk assessment.