Manager - Quantitative Risk Management
Manager, Quantitative Risk Management
The role will be part of the Quantitative Risk Management department, which is charged with researching, developing, implementing and supporting the Clearing House analytics used for risk and default management.
These analytics include in particular
- models (calibration, simulation, pricing, sensitivities, Value-at-Risk, liquidity, regulatory capital)
- testing frameworks (back-testing, stress testing, unit and regression testing)
- tools dedicated to clients' portfolio management (sensitivities, risk reports, margin adequacy, collateral)
The successful candidate will lead the quant team responsible for the OTC credit derivatives asset class. The position is based in London, with team members located both in London and in Chicago.
The role will require in particular to
- Research, design and improve the specification of models/algorithms
- Implement these analytics in the development library (C#) within a version control environment (Git)
- Implement automated tests (unit, regression) for these analytics
- Document both the mathematical specification and the code (Latex, Doxygen)
- Perform functional testing of these analytics (statistical analysis, back-testing, stress testing)
- Support the developers in charge of transferring the model to the production engine (C++) and review their implementation.
- Interact with both internal and external stakeholders. That stream includes attending product meetings with clients, presenting to risk committees, liaising with independent validators, and contributing to the regulatory approval of the models.
- Support the analytics once in production (ongoing monitoring, configuration control, operations support, clients queries)
- Manage the OTC credit derivatives team on a daily basis (planning, task supervision, HR)
These tasks apply at an individual contributor level, as well as a team supervisor and project manager. 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).
- 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)
- Experience in developing models (design and implementation) for pricing or risk management of derivatives,
- preferably in the fixed income asset class (credit or interest rates).
- Experience in writing model documentation and technical presentations.
- Experience with compiled, object-oriented programming languages such as C++ or C#.
- Good communication skills and the ability to manage a team.
The following would 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.
- Proficiency in R, VBA or SQL.
- Experience with code versioning systems such as SVN or Git.
- Experience with code documentation software, such as Doxygen.
- Experience in managing a team.
See Job Description