Sr. Quantitative Researcher

Sr. Quantitative Researcher

$ 50.46 /hour - paid weekly

Location

Bridgewater, NJ - 3 to 5 days a week in office

Start Date

ASAP

Contract Length

7+ Months *Contract Extension or Conversion to FTE based on Performance, Attendance, and Business Need*

Role Overview

This role focuses on designing and implementing mixed integer nonlinear optimization models to support portfolio construction, strategic and tactical asset allocation, and risk return optimization. You'll work at the intersection of quantitative finance, optimization theory, and large scale computation to build models that incorporate realistic investment constraints, nonlinear risk measures, and discrete allocation decisions.

Required Skills

• 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.

Preferred Qualifications

• Graduate degree (MS/PhD) in a quantitative field.
• 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.

Key Responsibilities

• Develop MINLP models for portfolio allocation, including:
• Cardinality constrained portfolios
• Transaction cost modeling (fixed + nonlinear costs)
• Nonlinear risk measures (e.g., downside risk, CVaR approximations, drawdown limits)
• Discrete investment decisions (e.g., lot sizes, minimum/maximum holdings, leverage rules)
• Implement optimization models using Python (Pyomo, CVXPY, Gurobi interfaces), AMPL, GAMS, or JuMP.
• Apply global and local solvers (e.g., BARON, Couenne, SCIP, Knitro, 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.