Location: Chicago,IL, USA
Client is the world's leading and most diverse derivatives exchange. The role will be part of the Client's Clearing Quantitative Risk Management department. Our Quants team are working with complex and advanced modelling and we're looking for someone ready for a new challenge to join the Chicago team.
The Manager Quantitative Risk Management is responsible for developing Risk/Pricing Models that evaluate counterparty exposures to the Clearing House. These include models related to Pricing, Value-at-Risk, Stress Testing, Liquidity, Regulatory Capital, & also developing tools for Portfolio Analytics. The incumbent also works to develop strategies to perform back-testing to ensure the adequacy of margin coverage & model assumptions.
Principal Accountabilities:
Conduct empirical studies and make recommendations on margin levels, modeling issues, and other risk-mitigation measures. Ensure that the model is up to date with the proven theories in the field.
Design and develop pricing and risk models across different various asset classes like Fixed Income Cash and Derivatives, OTC and Exchange-traded Futures and Options (e.g. Pricing, VaR, Backtest, Stress, Liquidity, etc.)
Ensure risk models meet the risk appetite across varying needs for coverage, ant-procyclicality as well as provide transparency, replicability and what-if capabilities.
Ability to do hands on programming in C++/Java, SQL as well as Cloud based platforms and work with financial developers and technology to deploy, test and continuously improve the models within the Production Infrastructure of Client.
Document and present results to Sr. Management, Risk Committees as well as regulators and end clients; work with internal and external model validators for governance needs.
These tasks apply at an individual contributor level, as well as a team supervisor and project manager because they entail mentoring junior quantitative and financial developers.
For instance, the successful candidate will be ultimately responsible for the long-term modelling strategy, and for the architecture of the development library (supported by a quantitative developer).
Qualifications
The requirements are for: Master or Doctorate in Computer Science, Financial Engineering, Financial/Applied/Pure Mathematics, Physics, or a related discipline.
Academic skills:
probability theory (including stochastic processes)
statistics (time series analysis, process estimation)
numerical methods (interpolation, integration, regression, root-finding, optimization, linear algebra, Monte-Carlo)
Fixed Income financial mathematics
Experience:
6+ years of experience in pricing complex derivatives and performing advanced statistical analysis on underlying risk factors.
Very strong expertise (3+ years) with Bond Mathematics, Fixed income Pricing and Risk modeling as well as with team management
3+ years in developing risk models (e.g. Historical VaR, Monte Carlo VaR, Multi-Factor Risk Models, Stressed VaR, and Liquidity Risk models) as well as model evaluation techniques (backtesting, sensitivity analysis, coverage statistics, etc.)
Experience providing theoretical justifications of risk models, for internal as well as external stakeholders. Also experience in developing risk model transparency and what-if analytics for risk managers, end users and regulatory stakeholders alike.
Experience in writing model documentation and technical presentations
The following would also be considered a plus:
Experience in developing the type of risk models used by clearing houses and market risk teams
Experience with modern OO libraries, implementing pricing or risk frameworks
Skills & Software Requirements:
Proficiency in programming languages such as C++, Python, VBA and SQL is essential