Vice President- IMD122118LJDI
Posted on Jan 17, 2019 by Goldman Sachs
A leading global investment banking, securities and investment management firm that provides a wide range of financial services to a substantial and diversified client base that includes corporations, financial institutions, governments and individuals.
- Multiple positions available. Design and manage multi asset class portfolios for various institutional investors across the world.
- Model various asset classes and perform portfolio optimization for long-term investment horizons. Research, design and build tools for managing glide paths for managing target date funds and Defined Contribution separate accounts.
- Provide customized strategic asset allocation advice for institutional clients.
- Design customized managed account strategies using stochastic optimization.
- Analyze economic data using statistical methods such as statistical filters, principal component analysis, and regression analysis to extract tactical signals and improve asset models.
- Develop external and internal tools for asset allocation and risk management.
- Ph.D. (US or foreign equivalent) in Physics, Mathematics, Electrical Engineering, Computer Science or a related quantitative field.
- Four (4) years of experience in the job offered or a related quantitative role.
- Must have four (4) years of experience applying the following quantitative techniques in scientific or investment research: software development for a large scale, multi-disciplinary project;
- Linear algebra; Statistics or signal processing or estimation theory; and Optimization.
- Must have three (3) years of experience: applying dynamic programming and reverse induction techniques; building models in financial markets, including pricing models for exotic derivatives, simulations of economic scenarios and design of risk factor models; performing quantitative risk management analysis, such as stress scenario analysis, derivative Greek calculations and hedge efficiency analysis, analysis of impact of non-normal distributions on correlation hedges;
- working with Monte Carlo simulations, scenario analyses and back testing in financial markets; and numerical analysis and parallelization techniques for solving large scale computational challenges.