What is a Quant? Decoding a Career in High Finance
This week, Dr. Randeep Gug, Managing Director of the CQF Institute, spoke to BFM 89.9 – The Business Station to unpack findings from our 2025 Careers Guide to Quantitative Finance. The discussion explored how core skills, markets, roles, tools, and soft skills are evolving and what that means for practitioners at every level.
Explore five key insights from the interview below and their practical implications for careers in quant finance.
1. Core skills are still critical, but adaptability is the differentiator
Quant finance is anchored in three disciplines – finance, mathematics, and programming. Finance is the domain the problem is in, mathematics is the language of the model, and programming enables quants to actually implement and find a solution. That foundation hasn’t changed - but with geopolitical events and volatility reshaping financial markets, employers want quants who can adapt those skills through changing realities, challenge assumptions, and retool models for new environments. In short, the most valuable quants are those that can apply this very technical skillset with situational awareness.
Lifelong learning is essential. That’s why, on the CQF, we offer all our alumni access to the latest full syllabus of the program regardless of when they graduated. We have been around since 2003 when the world was very different, but our alumni from then still have access to the full 2025 syllabus so they can upskill in the new areas.
2. Demand for quant skills is global, but with especial growth in Asia
Demand for quant talent is global, but is accelerating across Asia, with Hong Kong and Singapore at the fore. These hubs combine deep, liquid markets with easy proximity to growing economies, such as India, China, and Australia. This enables organizations to access diverse regional opportunities from one base. Just as important, they offer the ingredients high-frequency and systematic firms need to scale, including robust technological infrastructure, access to skilled local talent, and a supportive regulatory stance. For example, Singapore has balanced regulations for areas such as fintech and crypto. This makes these cities ideal locations for organizations to build out research, trading, and production engineering teams.
3. High compensation is seen in roles combing research with new ML techniques
As the industry becomes more technical, Quant Researchers and Machine Learning Researchers are in demand and offer high compensation. This is being driven by new tools in machine learning and AI. These areas are hot topics now, and organizations want quants that can, for example, develop and implement these techniques for trading or can maximize return by using the latest machine learning techniques for portfolio construction. It is worth noting, that quants have been using machine learning for many years – the CQF added data science and machine learning modules back in 2018 because these skills were in demand – but organizations want quants that can innovate and keep them at the forefront of financial markets.
4. Python is the dominant language now, but languages like C++ are still essential
‘Which programming language should I learn?’ is a question frequently posed by CQF delegates. For those that are new to coding, Python is the easiest to learn and prototype. Python also has rich libraries and an open-source ecosystem that make it ideal for testing ideas and validating research. However, when models must operate at scale, speed, and reliability - particularly in latency-sensitive contexts like high frequency trading - C++ remains essential. Many quant teams will prototype in Python and then port to C++ for deployment. For aspiring quants, the priority is learning to implement models well in Python, but learning C++ as you move closer to production systems will enhance your employability.
Master the fundamentals – math, programming, finance. Understand that you are solving a problem in finance so question the assumptions. Keep learning. Build a professional network as this will help you share ideas and ask questions and stay curious.
5. Soft skills and a healthy dose of skepticism are as important as technical skills
Following the financial crisis in 2008, quants must be able to explain what their models do, where they break, and why the assumptions hold - or don’t - in a given context. Being able to clearly communicate this to non-technical stakeholders is a vital part of a quant’s role. Equally, a degree of skepticism is key for quants looking to succeed. A common mistake of early-career quants is not questioning the models, which can lead to them being applied in the wrong situations. Quants must question the setup, stress the assumptions, and be explicit about limitations. Those who combine clear storytelling, judgment, and a willingness to challenge their own work will advance faster and build trust that compounds over time.
