Credit Risk Modeler @ Ford

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.

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

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