Credit Risk Modeler @ Ford

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. Good understanding of Probability of Default (PD), LGD and EAD modeling technique. Team player and collaboration skills. Programming skills in SAS, GCP, R 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

Masters in Mathematics / Statistics / Economics / Engineering or any other related discipline or a track record of performance that demonstrate this ability Practical applications of mathematical modeling, Operations Research and Machine Learning techniques Demonstrated knowledge in credit and/or market risk measurement and management Excellent problem solving, communication, and data presentation skills Proficient with SAS, SQL and GCP

Execute analytics special studies and ad hoc analyses Using SAS, GCP, R, 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 Evaluate new tools and technologies to improve analytical processes Set own priorities and timelines to accomplish projects (accountability for project deliverables)