Complete targeted quantitative analyses and build components of advanced models across the spectrum of Commercial Risk management, including prospecting, underwriting, portfolio monitoring, pricing, asset evaluation and loss mitigation.
Develop, monitor and implement commercial credit risk models to achieve compliance with compliance and risk strategy of bank.
Disaggregate and structure complex, often ambiguous, business problems and evaluate different analytical approaches to develop potential solutions. Apply advanced analytic and quantitative tools, statistical modeling techniques, and data mining procedures and tools to derive business insights and solve complex business problems.
Ensure a correct computation of IFRS9 Provisioning, Stress Testing for Credit Risk, and perform monthly Risk Cost forecasting for Commercial Portfolio and continuously improve the commercial model ecosystem.
Develop components of statistical models writing and developing scripts to improve commercial risk management decision making (e.g., underwriting models). Contribute to value chain problem solving through findings and insights from credit risk analysis and help facilitate data integration and management between the value chains.
Contribute to the development of advanced analytics knowledge bank wide, teaming up with other analytical units.
Qualifications:
Advanced degree preferred (MS, MA) in a quantitative discipline (typically statistics, mathematics, econometrics, economics, operations research)
Minimum 7 years of hands-on experience in analytical services, quantitative modelling, and/or validating oriented toward risk management in the banking sector, as well as/ or a PhD
Significant experience using statistical software packages such as SAS, STATA, SPSS, R and data query languages such as SQL
Proven track record of working in quantitative teams, with a high degree of independence and responsibility, in one of the following fields: credit risk, stress test and financial planning, corporate stress test, advanced analytics in nonfinancial risk
Demonstrated ability to employ advanced econometric or statistical methods in practice, including a working knowledge of machine learning techniques, and solid data handling skills
Ability to communicate complex ideas effectively – both verbally and in writing – in English
