Credit Risk Modeler
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Inetum is an agile IT services company that provides digital services and solutions and a global group that helps companies and institutions to get the most out of digital flow.
Vauban, the Romanian division of Inetum, is an important player in the IT services and solutions market in our country, with over 13 years of activity. Vauban has over 450 employees who provide, from the service centers in Bucharest, Pitesti and Constanta, IT consulting services, infrastructure and software development services, digital services, solutions for Smart City.
• Knowledge of Credit Risk Models – PD, EAD, and LGD, scorecards, IFRS 9, Basel II IRB approach for Credit Risk, Logistic and Linear Regression Models
• Strong experience in the credit risk model development from the scratch
• Good knowledge of R, Python (including OOP) and Statistical procedures. Knowledge of automation capabilities in R and Python
• Experience working with multiple datasets, cleaning of data and performing data analysis
• Excellent verbal and written communication abilities
• Ability to articulate ideas and develop recommendations
• Experience in working as a part of bigger teams working towards a common goal
• Proficiency in developing and giving presentations
• Strong client presentation skills
• Strong oral and written communication skills, including the ability to document analytical results suitable for audiences of all technical levels
• 5+ years of experience in Global Banks
• Strong analytical and interpersonal skills.
• The candidate needs to have a good knowledge of statistical methods and tools including logistic regression, Bayesian statistics, Markov chain process, time series analysis etc.
• Understanding of credit risk model constructs and different credit risk modeling frameworks
• Understanding of the consumption layer of the curated data, ie. Model development for credit risk models, especially IFRS9, Stress Testing and AIRB (PD, LGD and EAD models)
• Understanding of variables treatments, exclusions, transformations and data sources
• Model development experience using Python and R
• Adhoc data analysis and queries to support questions and process migration
• Prototype source data migration in established processes and production codes
• Help build competencies and develop training programs in credit risk.
The candidate will be working directly on credit risk models for a global investment bank together with the model development team of the bank and will work with them to improve the existing models. The role requires good understanding of credit risk models for wholesale portfolio, model structure, variable treatments, variable data framework and process efficiency in model development process.