Quantitative Analyst

The goal of a quantitative analyst is to apply mathematical and statistical methods to financial problems that are too complex to solve with intuition alone. This might mean finding the fair price of a derivative, estimating the risk of a portfolio, or identifying patterns in market data that can be exploited systematically.

The daily work involves a mix of research, model building, and implementation. You spend time reading academic literature, developing ideas, writing code to test them, and working with traders or risk managers to put models into practice. The work is more research-oriented than most finance roles. You are expected to understand why a model works, not just how to run it.

The main tools are Python and C++. Python is used for research and data analysis. C++ is used where speed matters, such as in pricing engines or live trading systems. Knowledge of stochastic calculus, probability theory, and time series analysis is important. Linear algebra and numerical methods are also regularly used.

The role is technically demanding and sits close to both finance and applied mathematics. At investment banks, quants often specialise in a specific asset class such as rates, equities, or credit. At quantitative trading firms, the focus is more on finding statistical edges in market data. The role connects closely to the Trader role, which uses the models quants build, and to the Risk Manager role, which applies similar quantitative methods to risk measurement.