Anna Senkevich, a doctoral graduate from the Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa), talks about her experience of doing a quantitative finance internship and how it was similar and different to doctoral research
Two years ago, I entered my final year of PhD in Statistical Applied Mathematics (SAMBa) and it was time for me to decide what I wanted to do after graduation. A career in finance always appealed to me and having a technical background and an interest in Asia I applied for a quantitative analyst (quant) internship with HSBC.
The application process for the internship consisted of CV submission, two online tests (immersion and coding) and an assessment centre (a few technical and a strength-based interview). There are many helpful tips and hints online, but tailoring your CV to the role and preparing for tests and interviews seemed to be the most important ones to me. As part of the preparation, I read about the company, the industry, practised answering questions, brushed up on coding skills and refreshed my knowledge of relevant technical topics. Talking to the Careers Service and preparing together with fellow students with similar career aspirations proved very helpful for me.
Based on the outcome of my interviews, I was offered the chance to join FX Spot, eRisk team: an electronic market maker, which facilitates trade in foreign currency, allowing customers to seamlessly exchange one currency into another. During my internship, I worked on assessing and improving some of the existing tools, which was a rather interdisciplinary project combining statistics, mathematical modelling and programming, and provided a great way to learn about the business and to get to know the team.
In a way, the format of the project was not too different from doing a PhD: it was technical, without a clear solution but with multiple possible ways to approach it. Beyond the technical knowledge and coding skills, the project relied on the ability to conduct research independently. However, the project was a lot more hands-on and goal-oriented with a clear deliverable objective at the end of my three-month internship. Moreover, the background setting could not be more different: specified working hours, business dress code and the fast pace and vibrancy of the trading floor were a constant reminder of the business applications and potential impact of the project’s outcome.
Throughout the internship there were plenty of opportunities to learn, to get exposure and to present my work to senior management. Being part of a larger cohort of interns made the experience more sociable. Everyone was placed at different desks throughout Markets & Securities Services (MSS), and so we worked on a variety of real-life problems. This way, we could draw upon each other’s experiences and learn about the range of problems faced by quant teams and possible solutions suitable for some asset classes but not for others. We were also encouraged to shadow experienced colleagues and to attend networking sessions held over lunch, to learn more about various products and business lines within HSBC. Moreover, there were a few programming workshops organised for us, to help us pick up relevant coding skills.
Doing an internship during my PhD was a great way to learn about opportunities to apply my technical skills to high impact real-life problems in the finance industry and to secure a permanent role in the team I knew I enjoyed working with.
This year HSBC is running a similar quantitative finance internship for PhD students. For more details see:
https://www.hsbc.com/careers/students-and-graduates/student-opportunities/quantitative-finance-internship
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