Financial Analyst - Advanced

Candidates who receive an offer will be required to successfully complete a background check and drug test as a condition of employment.

Title: Sr. Quantitative Researcher
Contract term: through 12/31/26
Location: Bridgewater, NJ - 3 to 5 days a week in office

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.

Key Responsibilities
• Develop MINLP models for portfolio allocation, including:
o Cardinality constrained portfolios
o Transaction cost modeling (fixed + nonlinear costs)
o Nonlinear risk measures (e.g., downside risk, CVaR approximations, drawdown limits)
o 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.

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:
o Multi period or stochastic asset allocation
o Robust optimization or scenario based modeling
o Machine learning driven return or risk forecasting
• Background in institutional investing, hedge funds, or asset management.

Metasys Technologies is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identify, national origin, veteran or disability status.