Data Driven Retail Credit Risk Specialist

5 days ago

Singapore beBeeRiskAssessment Full time $70,000 - $120,000

Job Opportunity:

Quantitative Analyst in Retail Credit Risk

 

As a seasoned Quantitative Analyst in Retail Credit Risk, you will be responsible for developing and maintaining advanced models to analyze credit risk within the retail sector. Your expertise in data analysis, statistical modeling, and technical skills will enable you to identify key drivers of credit risk and make informed decisions to mitigate potential losses.

 

Required Skills and Qualifications:

  • Advanced knowledge of statistical modeling techniques, including regression analysis, time series analysis, and machine learning algorithms.
  • Strong understanding of credit risk management principles and practices.
  • Ability to communicate complex analytical results to both technical and non-technical stakeholders.

 

Benefits:

In this role, you will have the opportunity to work with a talented team of professionals who share your passion for analytics and credit risk management. You will also have access to cutting-edge tools and technologies, as well as ongoing training and development opportunities to help you grow in your career.

 

Others:

Working closely with cross-functional teams, including risk management, finance, and operations, you will contribute to the development of strategic initiatives that drive business growth and improve customer satisfaction. If you are a motivated and detail-oriented professional with a strong background in quantitative analysis and a passion for credit risk management, we encourage you to apply for this exciting opportunity.

 

Key responsibilities include:

  1. Develop and maintain advanced statistical models to analyze credit risk within the retail sector.
  2. Collaborate with cross-functional teams to develop strategic initiatives that drive business growth and improve customer satisfaction.
  3. Analyze and interpret complex data sets to identify key drivers of credit risk and make informed decisions to mitigate potential losses.
  4. Communicate complex analytical results to both technical and non-technical stakeholders through clear and concise written reports and verbal presentations.

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