Credit Risk Modeler

Potential candidates should have knowledge in general math modeling, Machine Learning (ML) and related Operations Research 

(OR) and simulation techniques. Candidates should have hands-on experience in developing quantitative models. They should 

possess the ability to translate a business problem into an analytical problem, recommend, implement and validate quantitative 

models, and generate/deliver insights to stakeholders. Preference would be given to candidates that are intellectually curious, 

innovative thinkers, and have demonstrated ability to solve problems independently. 

Develop and validate credit risk models • Using SAS,Python for model building and model validation • Continual enhancement of statistical techniques and their applications in solving business objectives • Compile and analyze the results from modeling output and translate into actionable insights • Prepare PowerPoint presentations and document preparation for the entire credit risk modeling process • Collaborate, Support, Advise and Guide in 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 (accountability for project deliverables

Masters in Finance, Financial Engineering, Analytics or Mathematics, Computer Science, Statistics, Industrial Engineering, Operations research, or related field. • Good understanding of Probability of Default (PD), LGD and EAD modeling technique. • Very good understanding of Predictive modeling techniques and their application. • Knowledge of Credit life cycle • Statistics and machine learning techniques. • Conducted and applied statistical methodologies including linear regression, logistic regression, ANOVA/ANCOVA, CHAID/CART, cluster analysis • Team player and collaboration skills. • Programming skills in R, SAS, and PYTHON. • Fluency with Excel, PowerPoint and Word • Strong written and oral presentation / communication skills – must have the ability to convey complex information simply and clearly