Credit Risk Modeler - Chennai, India - Ford Motor Company

Ford Motor Company

Verified Company

Chennai, India

2 weeks ago

Posted by:

Deepika Kaur

beBee Recuiter

Description

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)
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
Familiarity with any of R, Python, Alteryx, GCP suite.
Experience with any of Qlikview, Tableau

Years exposure in Banking & Financial Services industry

Risk Analytics, Credit Risk Scorecard Development, Model Validation, IFRS 9 Validations, Credit Loss Forecasting
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.