Quantitative Analyst

DESCRIPTION:

Duties: Implement data driven and AI based Equity Exotic tools including parsing, pricing, execution, hedging, and back-testing.

Utilize data and quantitative techniques, including stats, NLP and machine learning.

Build data-driven hedging strategies for Equity Derivative products.

Build framework to allow large scale back-testing of real and hypothetical portfolios over historical and simulated market data.

Develop mathematical models for pricing, hedging and risk measurement of derivative securities within a quantitative library using C++ and Python programming languages.

Evaluate quantitative methodologies including identifying and monitoring model risk associated with derivative valuation models.

Support trading activities by explaining model behavior, identifying major sources of risk in portfolios and carrying out scenario analyses.

Develop GUIs that traders can use to execute and book the trades, hedge their positions and monitor daily risk and PNL.

Contribute from idea generation to production implementation: perform research, design prototype and implement analytics.

Design and onboarding payoffs to strategic platform for end-to- end quoting and booking.

Provide clear model documentation and work closely with the model review group to facilitate model approvals.

Support trading team and risk organization in pricing and risk management of equity derivatives.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Mathematics of Finance, Computational Finance, Mathematical Finance, Financial Engineering, or related field of study plus one (1) year of experience in the job offered or as Quantitative Analyst, or related occupation.

Skills Required: Requires experience in the following: Requires 1 year of experience in the following: Resolving Model financial mathematic problems, including Local Volatility, Stochastic Local Volatility, Jump risk and calibrate model parameters to market observables, using stochastics process and calculus, probability theory and measure theory; Designing and developing pricing engine and risk hedging applications for complex equity derivatives including Barrier Options, Variance Products, Cliquet, Rainbow Options, Autocallable and Structured Notes, and using modern C++ / Python; Designing and implementing Finite Difference PDE framework and Longstaff-Schwartz and Tsitsiklis-van Roy Monte Carlo engine for derivatives model library; Performing financial time series forecasting with EWMA model, ARIMA model, GARCH model and data visualization techniques; Performing financial statistical analysis leveraging machine learning method including multivariate regression, logistic regression, support vector machine, least angle regression, K nearest neighbors local regression and leveraging machine learning method such as K Means, hierarchical clustering, Principle Component Analysis; Conducting data analysis for large dataset using multithreading and multiprocessing; Performing fu...