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Strats

Location

remote

About the role

Strats refer to mathematicians, statisticians, computer scientists, and engineers who work in the financial services industry. In particular, strats are used to describe the mathematicians, statisticians, software developers, and engineers who work at investment banks in a front office role. The first strats team was set up within the strategies team at Goldman Sachs by its chief investment officer, Armen Avanessians, in the early 1990s. Historically, STEM (Sciences, Technology, Engineering, and Mathematics) graduates who wanted to work in financial services did so in various back-office roles. It was especially true for large investment banks, and it was rare to find a professional with a STEM background to be working in the front office of a major investment bank. In 1985, Goldman Sachs hired Armen Avanessians from Bell Laboratories. Avanessians was an electronic engineer and programmer who realized the growing importance of using mathematical algorithms to make decisions within financial markets. The strats team was born out of Avanessians' desire for closer collaboration between quants (i.e., specialists in mathematical finance and statistics) and engineers (i.e., computer scientists and programmers). Strats use mathematical and statistical tools to quantify the various kinds of risks faced by the bank. The different departments at an investment bank face different kinds of risks. For example, quants attached to the fixed income trading desk and quants attached to the equities trading desk may work with very different mathematical and statistical models, because the risks faced by the two groups are vastly different. The first generation of strats evolved from quantitative specialists within the exotic derivative pricing group at Goldman Sachs. The quants used advanced abstract mathematical concepts such as partial differential equations to accurately price exotic derivatives that did not have well-defined markets.

About the company

Corporate Finance Institute

Skills

programming
stochastic processes
financial modeling