Quantitative Research [Multiple Positions Available]

DESCRIPTION:

Duties: Drive end-to-end research, development, and enhancement of derivative pricing and cross-asset diffusion models focused on commodities and foreign exchange, including model testing and preparation of model documentation. Conduct ongoing model performance testing and serve as a point of contact during model review and re-review processes. Provide quantitative support to trading desks through analytical methodologies for profit-and-loss attribution, risk decomposition, and hedging evaluation. Develop frameworks that improve transparency of model behavior and risk factor dynamics. Collaborate with front office stakeholders on structuring and onboarding new transactions and asset types, including independent assessment and validation of counterparty credit risk metrics and exposure profiles. Interface with credit risk management functions to support pre-trade approval workflows and post-trade credit exposure monitoring in alignment with enterprise risk governance frameworks.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Financial Engineering, Mathematical Finance, Statistics, or related field of study plus three (3) years of experience in the job offered or as Quantitative Research, Quantitative Analyst, or related occupation.

Skills Required: This position requires three (3) years of experience with the following: Applying no-arbitrage pricing theory, stochastic calculus, probability theory, measure theory, reduced-form intensity models, and continuous-time stochastic processes to quantitative modeling and analytics across commodities such as North America and European Oil, Power, Gas, Emission, Base and Precious Metals, and foreign exchange markets including G10 and major emerging market currencies; Calibrating multi-factor models with seasonality and mean-reversion features using optimization algorithms to fit model parameters to observed market data; Developing and maintaining high-performance computational libraries using C++, including memory management, parallelization strategies, Monte Carlo methods, optimization methods, interpolation and approximation methods, stochastic differential equations, and regression methods; Quantifying counterparty credit risk metrics and regulatory capital measures to support risk management and governance processes; Constructing and deploying analytical tools for profit and loss attribution, risk decomposition, and hedging strategy evaluation using econometric methods, statistical learning, machine learning models, and automated workflow frameworks to support trading and risk management.

Job Location: 270 Park Ave, New York, NY 10017.

Full-Time. Salary:  $200,000 - $285,000 per year.