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About the job
The team focuses on developing data driven risk models and especially Capital and IFRS9 models working for advanced bank in Europe. Work calls for developing statistical models for credit risk and regulatory teams with stress on not just model development but also deliver end to end solution-ing to client problems and understanding appropriateness of actions from banks perspective.
Role Focus
- Identify potential problems, build the hypothesis, identify research data attributes and determine approach to solve analytical problems specifically in the area regulatory risk and modeling
- Understand, extract, clean and prepare the data for analysis
- Build complex programs for running statistical tests on data and for understanding correlation of various attributes. Perform detail exploratory data analysis on the data and report findings
- Compare and weigh numerical model outputs (preferably from SAS) and identify best choice models
- Build challenger models, build and validate models and ensure models with good predictive power as well as those which meet regulatory and audit standards.
- Make recommendations for procedural improvements supported by analytical findings
- Build presentations for targeted audiences
- Pragmatic approach to deliver fit to purpose strategies for bank under clear timelines.
Skill Requirement:
- Experience in Statistical Modeling with techniques like Linear Regression, Logistic Regression, Decision Trees within the gamut of scorecard development.
- Working knowledge of aspects like PD, LGD, EAD, impairment calculations, macroeconomic modeling, SICR, representativeness, back-testing will be given higher preference.
- Understanding of collections and recoveries tools such as Debt-manager etc. will be an advantage.
- Mandatory experience in SAS, Python or R
- Strong communication skills
Click here to Apply.
