An Interview with a Quantitative Analyst

Tell us about your current role?

I'm a Quantitative Analyst at the Bank for International Settlements. I work in the Risk Models team, which is a functional part of the Risk Management unit at the Bank. This team is responsible for maintaining and improving the existing core risk models for market risk, credit risk, and liquidity risk. We work on implementing enhancements to those models and potentially introducing new modeling solutions. At the same time, we oversee the pricing and valuation of various asset classes, and the methodology behind that. Finally, we support the business as well as senior management in various quantitative analyses. 
 

Where and in what role did you start your career and how did you get to where you are today? 

I started my career a little over 10 years ago at UBS in Krakow, Poland in the market risk operations team. I was mostly responsible for supporting market risk officers at UBS in their job. After two years, I became a treasury risk officer at UBS and this was a very good opportunity for me to learn about the broader business, to interact with the traders and other risk and financial control officers and participate in many bank-wide projects. I became very interested in moving to Switzerland and after some time, I found a risk analyst position at the BIS. At the beginning of 2020, I transferred to Switzerland. Within my first two years there, my manager at that time recommended the CQF to me, which was a significant moment in my career path. Later, after around 2 and a half years I moved to Risk Models team. 
 

What are some of the highlights of working in risk management and what are the biggest challenges?

The primary highlights are that we are a project team, with a very creative focus and lots of collaboration. The job itself gives a sense of accomplishment because we normally either create or enhance a model. In this sense, we are not a typical analyst or operational team, which executes processes that already exist; we are trying to improve or find some new solutions for a process. Personally, my initial challenge was patience, after having worked in more dynamic settings of risk operations or risk analysis. I wanted to solve issues immediately and this is not always possible when you're working on a quantitative task, especially when you're implementing new solutions. This kind of creative job requires patience, learning new concepts and eventually expanding expertise; for me these are the main challenges. 
 

Could you describe what a typical working day looks like for you in your current role?

I would say it's a good blend between various elements. On one hand, we have conceptual/theoretical work, implementation of solutions and adjusting the models in Python, or fulfilling requests from the senior management and business users. In addition, there is always a need to maintain our model documentation and create new documentation for models that have been developed. We also engage in project work where we interact with others and either serve as members of a working group, by being experts in the modeling field, or often lead projects ourselves. This involve collaborating with IT teams and analysis teams, as well as various other stakeholders, with help of whom we advance certain ideas and solutions that have a wider impact on the institution. 
 

What do you think are the most important skills for professionals in your field to have?

I think at the moment it is a little bit challenging, because the industry is changing so quickly, and new ideas also appear rapidly. On one hand, you are still required to have more traditional quantitative skills like asset pricing, risk measurement, and time series modeling. On the other hand, there is also a growing need for expertise in machine learning, or entire field of AI, which are slowly becoming an industry standard. Quants entering the field would most likely need to know how to interact and possibly implement, for example, supervised, unsupervised, deep learning or pre-trained AI models, and also how to utilize in general data science at the financial institution. I would say that the most important technical skill, alongside the mentioned skillset, is Python programming and/or working knowledge of other languages. Python is replacing the traditional analysis tools and without proper Python programming skills, it will be very difficult to progress in a career in quant finance. 
 

You earned the Certificate in Quantitative Finance (CQF). Why did you decide to enroll in the program and where has the CQF added value to your career?

My manager at that time at BIS told me about the program and I was intrigued by what I saw right away. I joined the January cohort in 2021, and I completed the certificate in 2022. The biggest added value was that it helped me transfer to a new role in the risk models team very smoothly, because this skillset was exactly what I needed to make that move. It also brings all aspects of quant finance together in a very coherent and compelling way.
 

Do you think the industry has changed since you started your career, and how do you see it changing in the next few years?

I think it has changed very significantly. First of all, in recent years we went through the pandemic and significant economic challenges, as well as global geopolitical tensions. As a result, we moved from a low-interest rate environment to a higher interest rate environment, which introduced another wave of challenges for the industry. At the same time, we continue to go through the technological and digital transformation. AI has entered the stage, through automation a lot of business processes, and with the growth of online banking services, finance is becoming more decentralized. The regulatory landscape has also grown significantly, because of expansion of the finance industry. In my opinion, over the next few years, the data science and AI will be at the center; this technology is accelerating and spreading through various industries, not only finance. It's going to be at the heart of future development.
 

What would your advice be to someone starting a career in your field today?

The default skillset would be, in my opinion, knowledge and skills in finance, mathematics, statistics, and Python. I think, due to a growing demand for expertise in AI, it would also be beneficial to complete studies or courses in machine learning and AI technologies. This would be a tremendous leverage, especially if a potential candidate for a job position can think about how this technology could be used in practice. Apart from these technical skills, it is crucial to think about collaboration and cooperation, especially when remote working became part of our day-to-day jobs. I think it's very important to not to neglect in-person, as well as online interaction with people. It's not only about doing the job, but it's also about how you do it, especially when others are involved. Efficient and open communication, as well as clear and imaginative presentation of ideas is crucial. Here creativity also counts.