Quantitative Risk Analyst

Quantitative Risk Analyst

As a quant contractor you will be responsible for working in a team that develops 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, and also developing tools for Portfolio Analytics (Sensitivities, Risk Reports, Margin Coverage, etc.). This senior analyst will also work to perform back testing and statistical analysis required to ensure the adequacy of margin coverage and justify other model assumptions.

In terms of the asset classes, this role would fit someone with academic knowledge or experience in either OTC (IRS, FX, and CDS) or Commodities/Futures asset classes. In terms of financial math skills, it requires a good knowledge of advanced pricing models (Options) and risk methods like Monte Carlo, Volatility Forecasting, Correlation Analysis, Liquidity Risk, etc. Expertise in statistical testing and prior experience with theoretical justifications of Risk Models is also a helpful experience for this role.

This position will also entail significant interaction with the Clearing Technology Department to implement, test and maintain these risk models. Also very critical is the ability of the staff to be able to learn the understand core business principles related to Dodd-Frank and other regulatory principles as they relate to Clearinghouse Risk Management policies. As such, this role would require the ability to multi-task and operate under aggressive deadlines.

Qualifications:

- MBA/MS or PhD in Finance, Economics, or a quantitative field and possesses strong quantitative, analytical and problem solving skills
- Strong knowledge of pricing complex derivatives and performing advanced statistical analysis on underlying risk factors (returns' distribution, volatility, correlations, etc.)
- Preference will be given to candidates who can demonstrate the best practices in developing risk models like Historical VaR, Monte Carlo VaR, Multi-Factor Risk Models, Stressed VaR, Liquidity Risk models, etc.
- The candidate should also have had academic experience in probability theory, stochastic processes, and have experience providing theoretical justifications of Risk Models they have developed.
- Experience with some programming languages such as C++/C#, R, VBA and SQL is also required.

McGregor Boyall is an equal opportunity employer and do not discriminate based on race, religion, gender, age, sexuality, gender identification, or physical ability.