Senior Quantitative Analyst

Location: Dallas or Tampa or Jersey City NJ (Hybrid)

Duration: 12 Months Contract

Experience: 5 Years

Education level: Bachelors degree

Job function: Information Technology

Skills Required: Quantitative Models Research VaR and Backtesting SQL Python VaR Modeling Quantitative Risk Management

Note: HM is looking for candidates who are near to any locations (McLean VA or Dallas or Tampa or Jersey City)

Job Summary

We are looking for a consultant to join the Quantitative Risk Management group (QRM) which is responsible for quantitative model development and performance assessment including model performance monitoring (MPM) and backtesting (BT).

The consultant will support the backtest and MPM process. Specific Responsibilities:

• Design develop and maintain backtest model.
• Assist with backtest reporting and diagnostics
• Assist with ad hoc model risk analyses as needed

Qualifications:

• 5 years of working experience and must have 3 years of handson experience in quantitative models research with deep understanding on VaR and backtesting as well as statistical applications.
• Excellent communication skills both oral and written.
• Must have excellent interpersonal skills
• Selfmotivated and able to work independently.
• Have a general knowledge about the financial market products risk management (such as VaR modelling) and risk metrics (such as backtesting).
• Solid programming skills in data processing language such as SQL Python.
• A Masters degree in a quantitative field preferably in applied economics econometrics statistics or financial engineering PhD with similar background is preferred.

Must have:

• Demonstrated experience in quantitative model development and performance assessment particularly in areas such as Value at Risk (VaR) and backtesting methodologies.
• Proficiency in data processing languages such as SQL and Python essential for data analysis model development and automation of tasks.
• General understanding of financial markets products and risk management principles including VaR modelling and risk metrics such as backtesting
• A Masters degree in a quantitative field preferably in applied economics econometrics statistics or financial engineering. A PhD with a similar background is preferred