Credit Risk Modeler

1 week ago

Chennai, Tamil Nadu, India Ford Motor Company Full time ₹ 1,40,000 - ₹ 28,00,000 per year

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

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