Credit Risk Modeler

13 hours 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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