The Risk division is a highly visible, dynamic area of the firm where you can be an integral part of decisions making that supports the bank’s business.
Our responsibilities range from enterprise risk management to risk and finance reporting and regional risk teams covering the risk management for our entities.
The Risk division's long-term success depends on our ability to achieve our vision and fulfill our mandate. Ultimately, this depends on the skills, experience and engagement of our employees.
We offer a collaborative and entrepreneurial environment that offers direct contact with senior management and encourages leadership at all levels.
Team and responsibilities :
The Models and Methodology team is responsible for developing internal models used to compute risk management metrics used by the business and other risk divisions as well as the development and maintenance of models used to compute the market risk capital of the bank.
You will be part of the global macro team covering FX, rates and commodities
You will develop the necessary tools and platforms to automate the back-testing of existing and new models needed for regulatory purpose or by the internal partners
You will work globally across multiple projects which all have high exposure to the business
You will interface the Front office analytics and build the necessary interfaces to ping the necessary valuation functions along with the necessary underlying data (market data, historical data and static data)
We offer an unusual opportunity to develop a generic back-testing platform to support the bank’s needs around the SR117 commitment covering the global-macro scope
Open to discussing flexible / agile working.
You hold a degree in Mathematics, Engineering or a Scientific Discipline.
You have strong C#, Python, Java coding skills.
You are experienced with IT implementation.
You have outstanding mathematical and analytical skills.
You feel comfortable with your fluent English skills.
Experience as a Data scientist and deep understanding of statistical modeling would be ideal.
Knowledge of value at risk, Rates, Fx, Commodities pricing models is a plus but not required.