Quantitative Researcher, Portfolio Optimization

Responsibilities

• As an integral part of the Quantitative Research and Investing (QRI) group, the portfolio optimization team is responsible for research into mathematical techniques to improve the performance, net of taxes, of client portfolios and funds.
• You will design novel mathematical approaches to solve difficult portfolio optimization problems efficiently and reliably.
• Work closely with research teams to understand the breadth and scope of their optimization problems, to correctly model them in a research environment.
• Deliver heuristics and reformulations which allow solvers to quickly converge to optimal solutions for specific classes of such problems, for both taxable and non-taxable portfolios.
• Partner with team members to direct the implementation of your findings in the portfolio optimization system.

• A Master’s degree or higher in computer science, engineering, or mathematics, with extensive coursework in optimization.
• 5+ years in portfolio optimization-focused roles, researching the algorithms and mathematics that support optimization, with direct experience using general purpose solvers (Gurobi, CPLEX) or proprietary solvers (e.g. conic).
• Demonstrable track record delivering mathematical solutions to common portfolio optimization challenges, including Integer Programming, using linear algebra, and implementing heuristics for more efficient integer solutions.
• Deep understanding of portfolio construction for taxable mandates and efficient heuristics and approximation algorithms for this problem class.
• Substantial exposure to mathematical techniques specific to portfolio optimization which take advantage of the structure of portfolios, risk, and tax to find optimal solutions efficiently.
• Track record delivering optimization solutions for clients in financial services with direct experience working with clients, synthesizing their high-level needs with implementation in mathematics and working prototypes.
• Proven ability to take a research question from inception to a positive outcome independently, with appropriate scientific rigor.
• Sufficient fluency in technologies and tools to work independently.
• A consistent record of architecting sophisticated mathematical solutions to work efficiently and reliably in critical applications.
• Ability to recognize business risk and surface it to key decision-makers.
• Collaborative approach and ability to work with partners with different skills and expertise to deliver complex, reliable, scalable solutions to portfolio optimization problems.