Proficiency in probability, statistics and optimization.
Understanding of back-testing frameworks, historical analysis and scenario-based research.
Hands-on programming experience in Python (numpy, pandas, matplotlib...) or analytical packages (R/Matlab) and data visualization.
Currently pursuing a Master's degree or PhD
Local to New York
Sponsorship Qualifications:
Please note that our company is unable to provide employment sponsorship for this position and can only consider candidates who are legally authorized to work in the United States without sponsorship assistance (CPT, H1B, F1, L etc.).
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CME Group: Where Futures are Made
At CME Group, we embrace our employees' unique experiences and skills to ensure that everyone's perspectives are acknowledged and valued. As an equal-opportunity employer, we consider all potential employees without regard to any protected characteristic.
Benefits
Health insurance
Additional Information
CME Group is currently looking for a Quantitative year-found intern in our New York office.
This candidate will assist our quantitative risk research on day-to-day activities in support of CME Securities Clearing business. He will work 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. The incumbent also works to perform back testing & statistical analysis required to ensure the adequacy of margin coverage & justify other 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.
Ensure deployment, testing and continuous improvement of these models within the Production Infrastructure of CME.
Work on a team that enhances existing risk models as well as designs/prototypes new models across different asset classes like OTC and Futures (e.g. Pricing, VaR, Backtest, Stress, Liquidity, etc.).
Required Qualifications:
A Master or PhD in Statistics, Mathematics, Physics, Operational research, Financial math or Engineering.
Experience with some programming languages such as Python/C++/R/VBA and SQL is also required.