Optimization Quantitative Researcher – Neutrality

Optimization Quantitative Researcher – Neutrality

Requirements

• Master’s degree in Mathematics, Physics, Statistics, Operations Research, Financial Engineering, a related field, or foreign equivalent
• 2 years of experience as a Quantitative Researcher focusing on areas that can increase returns and reduce costs
• 2 years of experience with Mosek optimization and Fusion API
• 2 years of experience with at least 3 additional optimization tools & solvers such as: CVXPY, Gurobi, SciPy, CVXOPT, Bayesian Optimization
• 1 year of experience with KDB+/Q
• 2 years of experience using Python and Pandas for processing large data sets
• 2 years of professional or academic experience in conic and nonconvex optimization
• 2 years of professional or academic experience in reinforcement learning, specifically in resource allocation and experimental design
• 2 years of experience in global equities

Responsibilities

• Work closely with other researchers and portfolio managers to optimize intraday global equities strategies to increase overall returns
• Design, develop, and backtest optimization algorithms and libraries that can be expanded and generalized for the usage of other teams at Schonfeld
• Take inputs from various quantitative models such as trade-cost model, barra risk models and others, and combine to apply to a wide range of global equities strategies in order to minimize cost and increase risk adjusted returns
• Leverage Schonfeld’s top-notch databases, backtesting, and optimization infrastructure to develop models around alphas, execution, and risk management
• Design, build and backtest optimization-based alphas to diversify the current strategy library
• Evaluate and experiment with other optimization tools to upscale Schonfeld’s optimization implementation

Preferred Qualifications

No preferred qualifications provided.