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Credit Risk Modeler
4 weeks ago
Job Description
We are seeking an experienced Quantitative Modeler with a strong foundation in general mathematical modeling, Machine Learning (ML), Operations Research (OR), and simulation techniques. The ideal candidate will have hands-on experience in developing quantitative models and the ability to translate complex business problems into analytical solutions, deliver actionable insights, and validate models effectively.
Required Qualifications:
- Master's degree in Finance, Financial Engineering, Analytics, Mathematics, Computer Science, Statistics, Industrial Engineering, Operations Research, or a related field.
- Good understanding of Probability of Default (PD), LGD, and EAD modeling techniques.
- Very good understanding of Predictive modeling techniques and their application.
- Knowledge of Credit lifecycle Statistics and machine learning techniques.
- Experience applying statistical methodologies including linear regression, logistic regression, ANOVA/ANCOVA, CHAID/CART, and cluster analysis.
- Programming skills in R, SAS, and PYTHON.
- Fluency with Excel, PowerPoint, and Word.
- Strong written and oral presentation/communication skills ability to convey complex information simply and clearly.
- Demonstrated ability to translate a business problem into an analytical problem.
- Intellectually curious, innovative thinker, and proven ability to solve problems independently.
- Strong team player with excellent collaboration skills.
Key Responsibilities:
- Develop and validate credit risk models using SAS and Python for model building and validation.
- Continually enhance statistical techniques and their applications in solving business objectives.
- Compile and analyze results from modeling output and translate into actionable insights.
- Prepare PowerPoint presentations and documentation for the entire credit risk modeling process.
- Collaborate, support, advise, and guide in the development of the models.
- Acquire and share deep knowledge of data utilized by the team and its business partners.
- Participate in global conference calls and meetings as needed and manage multiple customer interfaces.
- Execute analytics special studies and ad hoc analyses.
- Evaluate new tools and technologies to improve analytical processes.
- Set own priorities and timelines to accomplish projects, demonstrating accountability for project deliverables.
