Senior Quantitative Researcher

Responsibilities

• Develop mixed integer nonlinear programming (MINLP) models for portfolio allocation, including cardinality constrained portfolios and transaction cost modeling.
• Implement optimization models using Python (Pyomo, CVXPY, Gurobi interfaces), AMPL, GAMS, or JuMP.
• Apply global and local solvers such as BARON, Couenne, SCIP, Knitro, and Ipopt to solve non-convex allocation problems.
• Build custom heuristics, relaxations, or decomposition approaches to improve scalability for large universes.
• Integrate optimization models into portfolio management systems and backtesting frameworks.
• Conduct scenario analysis, stress testing, and sensitivity analysis on optimized portfolios.
• Collaborate with investment teams to translate portfolio constraints and investment policies into mathematical formulations.
• Document methodologies and present results to quantitative researchers, portfolio managers, and risk teams.

• Strong background in optimization, quantitative finance, operations research, or applied mathematics.
• Experience with nonlinear programming, mixed integer programming, and global optimization.
• Proficiency in Python or Julia for modeling and data analysis.
• Familiarity with portfolio theory, risk modeling, and financial instruments.
• Ability to work with large datasets and high dimensional optimization problems.
• Understanding of convexity, duality, and numerical stability in optimization.

• Experience with multi-period or stochastic asset allocation, robust optimization, or scenario-based modeling.
• Machine learning-driven return or risk forecasting.
• Background in institutional investing, hedge funds, or asset management.