Rain Vagel: Using algorithms learned during the master’s programme in computer science in Wise

Rain Vagel graduated with a BSc degree in computer science from the Institute of Computer Science, University of Tartu in 2017. After serving a year in the defence forces, he continued his master’s studies in the same field, this time specialising in data science, which he completed cum laude in 2020. While still at the university, Rain applied for a software engineer internship at Wise (formerly TransferWise), and today works as a data scientist for the same company, collaborating extensively with its international teams and leading key projects.

I have always been fascinated by IT. As a young boy, I used to play computer games and wonder how game worlds are made. You write a few lines of code somewhere and stories and characters come into being. It’s as if whole worlds are created out of nothing. Machine learning and AI captured my interest already back then – mainly for the reason that this type of work doesn’t necessarily have to result in a game. You can make machines do things that actually help people and make the world a better place for the future.

Even today, many people continue doing simple, mind-numbing work. I know the feeling–- one summer I was working at a meat factory, throwing chicken fillets onto a conveyor belt and cutting open meat bags. It’s practical to automate such tasks, as well as those that require big data analysis using machine learning. Banks, for example, need to keep track of a lot of data for detecting crime. Here a machine can perform an initial analysis so that human workers can move on to tackling more essential issues.

At Wise, I currently work in the team developing anti-money laundering systems, where we also mitigate risks related to human trafficking and terrorist financing. My work is very closely linked to what I studied at the university. A mathematical understanding and knowledge of how algorithms are created provides an additional benefit. I remember well a master-level course on algorithms taught by Jaak Vilo. It was a difficult subject with long lectures and a challenging amount of homework, but every aspect of it was tested out practically. Each student had to make a poster presenting their solution to a problem. In Wise, I’m now developing certain products using some of the algorithms I learned at this course.

During university studies, you often need to formulate ideas and present them to a larger audience. This helps to improve communication skills and express your ideas, which, in turn, will benefit you greatly if at any point in your professional life you need to create a vision, present it to others and then put it into practice. Currently, I have a number of projects of this kind, in which I need to explain to people from other areas of life how my idea can help us achieve our goals more efficiently.

My studies at the university cemented my interest in machine learning. Knowing what you want to do helps you move towards what you are interested in from the very first university years. Here’s another thing: you can get involved in interesting projects already before graduating. For example, I was briefly involved in an EstCube space project, but now students have the chance to assist in teaching a self-driving car to drive or develop military robots. The opportunities are endless and you can get involved in exciting projects.

Still, you need to put in considerable effort to get to all these fascinating subjects. At undergraduate level, maths was very challenging from the start and it never really became easy for me. Everything I had learnt during the 12 years of secondary school was summed up in a single lecture and from then on maths got “real”. Machine learning is basically a statistical phenomenon, so if you are interested in this subject, studying mathematics is worth it.

I knew right away that I was not going to go into research. To work in a company, you need to know whether you are more of an analyst or an architect. Data science, which is my field, remains somewhere in between the two, right next to product developers and leads. The best way to learn about your abilities is to do internships, get involved in all sorts of projects, or take part in hackathons. Even knowing what is not your area is important. During my undergraduate studies, I was an intern in a company developing business software and business intelligence. I discovered that this type of software developing was not my cup of tea. During my master’s studies, I got an internship as a data scientist at Wise and later stayed on to work there.

When studying computer or data science, you have to choose your internship placement carefully. There’s a good chance that it will become your future workplace, at least this was my experience. A good internship can teach you a lot. Be sure to look at how well the internship is organised in the company and whether it will result in a measurable outcome.

The time spent outside the classroom has also proved greatly beneficial for my work. During my undergraduate studies, I was actively involved in the activities of the student association MITS, where I met a great group of people who I still keep in touch with. It is also useful to meet people from other areas of life. People working in the IT share relatively similar views, but understanding a lawyer’s or a biologist’s thought processes helps broaden your worldview. In such conversations, you will learn about the trends and challenges of these disciplines and also how IT could be used solve these needs. True innovation tends to come from an in-depth knowledge of a field.

All in all, I really enjoyed my life at the university. Tartu is a great place to be and at the university anything seems possible. After starting a work career, you’ll inevitably face real-life constraints in time, money and even computing power. But the world is moving steadily towards greater automation. We have ever-increasing computing resources at our disposal, and a growing understanding of how to exploit machine learning to benefit humanity.

I believe that the young people who have decided to come to the university are choosing well for themselves and will create and implement visions that will make future life better for all of us.

  • Rain Vagel graduated cum laude as a computer scientist in 2020 from the Institute of Computer Science, specialising in data science.
  • During his studies, he was offered an internship at Wise and currently works at the same company as a data scientist.
  • Rain’s hobbies include rock climbing and trekking.
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