What is a quant developer?
Quantitative developers, sometimes called quantitative software engineers, focus on developing, implementing, and maintaining quantitative models. They tend to collaborate with quantitative analysts on the research side, and software engineers on the technology side in investment banks, hedge funds, and other financial firms. Quant developers are skilled programmers, with proficiency in languages like Python, C, C++, C#, and Java. They may also use mathematical and statistical software packages such as MATLAB, R, or SAS. Job descriptions for quant developers include developing and maintaining programing libraries, creating numerical library components, performance tuning of these libraries, and consulting on high-performance computing initiatives, optimization, and strategy.
Quant developers at work
Quant developers who work in investment banks or hedge funds may be found in three main locations: the front office, the middle office, or the back office.
Front office developers work closely with desk quants, traders and portfolio managers on the implementation and optimization of models. They will be skilled in writing and adapting code and as part of the trading desk implement the ideas and mathematical models needed to achieve financial success. They are a key part of any front office team and will often progress into quant or trading roles.
Middle office developers work on the technology systems that support the functions of the front office and the trading infrastructure of the bank or hedge fund. They work on large systems that execute the analytical and trading models and interface with the financial markets. Middle office developers also work closely with risk management and areas such as model validation.
Back office developers focus on accounting, operations, and compliance functions. They may also support back office automation activities.
Regardless of which office they work in, quant developers rely on strong knowledge of several domains: computer science and scientific computing; quantitative finance and applied mathematics; and statistics and statistical modeling. They also often work with data and data structures. Some firms today are implementing machine learning techniques in their research, requiring knowledge of machine learning tools and data science methods as well.
In addition to strong programming skills, quant developers should understand financial and quantitative models, which they will develop and maintain, with applications in analysis, trading, portfolio optimization, risk management, and performance measurement. This requires in-depth knowledge of math and statistics used within finance.
Quant developers will also benefit from having good communication skills to convey important information across diverse teams that may include quant analysts, traders, portfolio managers, and risk managers. Taking the time to develop the “business” skills required is helpful in a challenging role in a fast paced and demanding workplace.
Are quant developers in demand?
Quantitative developers are very much in demand in investment banks, asset management firms, hedge funds, and other areas of the financial industry. Since these roles require substantial knowledge of programming, math, and finance, the current supply of such professionals does not meet the demand. In addition, as many firms are embracing machine learning and data science, the need for quant developers with experience in AI and machine learning, large datasets, and alternative data in on the rise. Quant recruiters note that the job prospects for quant developers is strong and this is expected to continue into the future. So, how can you become a quant developer?
Quant developer training
It is important to seek out programs that focus on developing a full quant skill set: the math, the programming, and the finance. The Certificate in Quantitative Finance (CQF), delivered by Fitch Learning, does just that. The program provides a solid foundation in both the mathematical theory behind the most prominent models, but also the practice and how these are implemented in industry. Each lecture focuses on an explanation of the models and a critique of their strengths and weaknesses, highlighting where there is room for improvement. The curriculum also includes two modules dedicated to data science and machine learning.
The CQF also helps delegates develop proficiency in Python programming with weekly Python Labs where delegates practice building and implementing the models they have learned in the lectures. If delegates are new to Python at the start of the program, they can learn Python from scratch with the Python programming primer. At the end of the program, each student completes a final practical project, where they implement and analyze a real-world quantitative model to ensure they can apply their new skills to current industry problems. As many CQF alumni note, the program is a transformational experience that has played a key role in their professional development.
Download a brochure today to find out more about the program and how it could enhance your quantitative finance skills.
Find out more about careers in quantitative finance
If you are interested in becoming a quantitative developer, explore the new CQF Careers Guide to Quantitative Finance. Learn more about the skills needed and average salary you can earn in North America, Asia, and Europe for key career paths in quantitative finance.