Company Name

Ford ( Chennai )

Job Description

• • 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.
  • Proven hand-on experience in Artificial Intelligence and extensive experience in GCP .
  • 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 GCP, 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.
  Responsibilities
  • Develop and validate credit risk models
  • Enhance the usage of Artificial Intelligence / ML and transform into more efficient solutions.
  • Using GCP, SAS, 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
  • 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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