Global Banking & Markets-New York-Associate, Quantitative Engineering-10452362
$150,000–$189,000 year
On-siteNew York City, New York, United States or New York, United States
Job Summary
Develop, implement, and document economic and financial scenarios for businesses within the Firm. Collaborate with internal stakeholders to analyze user needs, addressing data, model, and implementation issues. Analyze large structured and unstructured data sets to build predictive models of market variables. Develop, refine, and improve scenarios by leveraging knowledge in financial markets, economics, and statistical analysis. Build and challenge risk models to identify and quantify vulnerabilities across market, credit, and liquidity risk. Create and maintain technical documentation of risk-model performance testing approaches. Master's or Bachelor's degree in quantitative fields with one or two years of experience, respectively, and proficiency in C++, Java, or Python. Annual base salary of $150,000 - $189,000 for this New York-based Associate, Quantitative Engineering role.
Required Qualifications
- Master's degree (U.S. or foreign equivalent) in Financial Engineering, Financial Economics, Applied Mathematics, Data Science, Operations Research, or a related field
- Bachelor's degree (U.S. or foreign equivalent) in Financial Engineering, Financial Economics, Applied Mathematics, Data Science, Operations Research, or a related field
- One (1) year of experience in job offered or a related quantitative engineering role (with Master's degree)
- Two (2) years of experience in job offered or a related quantitative engineering role (with Bachelor's degree)
- Five of the seven following skills: C++, Java, or Python
- Developing probability and pricing models utilizing financial mathematics principles, including stochastic calculus, no-arbitrage pricing theory, partial differential equations, multivariable calculus, linear algebra, numerical methods, optimization, probability, or random processes
- Quantitative analysis and model development using advanced econometric, statistical, and mathematical techniques, including Bayesian analysis, time series analysis, or machine learning algorithms
- Performing risk management or scenario-based analysis
- Developing quantitative risk analytics, including factor models
- Developing rigorous and scalable data management and analysis tools to provide risk oversight and support the investment process
- Statistics and data driven performance analysis, including Linear Regression or Time Series Analysis to measure performance
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