Optimization Quantitative Researcher (Neutrality) Position Available In New York, New York

Work closely with other researchers, and portfolio managers to optimize our intraday global equities strategies to increase overall returns. The optimizations will spend reduction in trade cost, risk and drawdown while increasing the positive returns of individual strategies and overall portfolio.

Daily job duties include:

• 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; and
• 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.

Requires a Master’s degree in Mathematics, Physics, Statistics, Operations Research, Financial Engineering, a related field, or foreign equivalent, plus 2 years of experience as a Quantitative Researcher focusing on any areas which can increase returns and reduce costs, including optimization to reduce intraday risk and transaction and reinforcing alpha signals within the financial services industry.

Experience must include:

1.2 years of experience with Mosek optimization and Fusion API;2.2 years of experience with at least 3 additional optimization tools & solvers such as: CVXPY, Gurobi, SciPy, CVXOPT, Bayesian Optimization3.1 year of experience with KDB+/Q;4.2 years of experience using Python and Pandas for processing large data sets.5.2 years of professional or academic experience in conic and nonconvex optimization6.2 years of professional or academic experience in reinforcement learning, specifically in resource allocation and experimental design7.2 years of experience in global equitiesPart time telecommuting permitted.

Wage Range:

$215,000 – $257,100CV to Mari Merlo [mmerlo@schonfeld.com], Ref. LX25

Minimum Salary:

215000

Maximum Salary:

257100

Salary Unit:

Yearly