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.
Valuable 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, Power Point, 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, cutting-edge thinker, and proven ability to solve problems independently.
Robust 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 Power Point 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 recent tools and technologies to improve analytical processes.
Set own priorities and timelines to accomplish projects, demonstrating accountability for project deliverables.

📌 Credit Risk Modeler Chennai
🏢 Ford
📍 Chennai