Fork in the road. Photo by Caleb Jones.
As my first blog post, I thought I’d look back on the last couple of years for some introspection.
After only about six months after starting my full-time job post-graduation did I think about going back to school. I had spent 4 years studying mathematics and statistics and at the two-year mark I was sure that I wanted to pursue a career in quantitative finance. It seemed to be the perfect way of mixing mathematics, engineering and business, with the glitz and allure of profit at the end of the day. That was the front-office allure, anyway.
In the back-office, there was less glitz, less pressure, and, arguably, less intellectual stimulation. The ‘bleeding edge’ research going on in the front office world was not particularly exciting either. Attempts at using cutting edge deep learning on ‘traditional’ option pricing problems did not break much ground. The hedge funds were still up on algorithmic trading, but those environments were even less attractive to me, as the sole purpose was truly profit and profit only. At least at a bank you could justify your existence by saying that you are serving clients.
And with those thoughts, I decided to broaden myself out with the well-known means of going back to school. I was still barely out of school, so I thought the transition would not be so bad. I had set my eyes on numerical analysis and computational mathematics as a topic to get my hands into, so a master’s program in Computational Science and Engineering at EPFL seemed to fit the bill. Getting back in the classroom was actually much harder than I would have thought, but I eventually settled in. The downsides of leaving your job are pretty obvious: you lose your salary, potential career progression and time, which at this point in one’s career is quite valuable from a CV point of view.
Although numerical analysis and computational mathematics was still on offer at the university, it seemed like everyone and their dog was trying to get their hands in two things: machine learning and data science. From a statistics background, the former did not seem too foreign a topic for me. The latter, neither, as data wrangling and processing was the start of any quantitative finance pipeline in industry. It was with this momentum that I pivoted from my former plans and directed them towards data science specifically. Software engineering and computer science was still something quite foreign to me, and looking back I wished I had just bitten the bullet and taken a few more of those classes.
Nevertheless, after 2 years in that program, I managed to score my current role as a Data Science Engineer. I don’t think I’ll shake the impostor syndrome I have with many software engineering tasks, but perhaps less and less over time. As opposed to my previous jobs, I’m learning a lot every day, both on my own and from my team. Overall, I’m really glad that I decided on taking a shot at this career sea change and I’m curious to see how my career develops from here on out.