Credit Risk Modeler

2 weeks ago

Chennai, India Ford Motor Company Full time

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.
- 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
- Experience with developing and implementing cloud based analytical solutions in GCP or similar set up.
- Ph.D. or Masters in Mathematics/Statistics/Economics/Engineering or any other related discipline or a track record of performance that demonstrate this ability
- Good exposure to ML techniques such as Clustering/classification/decision trees, Random forests, Support vector machines, Deep Learning, Neural networks, Reinforcement learning, and related algorithms
- Demonstrated knowledge in credit and/or market risk measurement and management
- Excellent problem solving, communication, and data presentation skills
- Proficient with SAS, SQL
- Develop and validate credit risk models
- Using SAS, R, Python for model building and model validation
- 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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