How I spent SberSeasons: four stories about different specialties

    They say that work in a bank is boring, and the internship is monotonous - you sit and sort through pieces of paper. We do not agree with this. And we hope that participants in SberSeasons, a paid internship for future graduates in technical and mathematical specialties, do not agree with this. Let's check it out and distract a little from the work of four interns from different departments - a server programmer, a risk model developer, a data scientist and an analytical expert.

    Nikita Batrukhin - Java Programming

    - I dreamed of working as a class server programmer from the seventh. And not disappointed. The work carried away, and I even spent the weekend in the office. He attended all the additional classes that we had twice a month - in other areas and topics, for example, Docker.
    I came from Kirov for an internship. Now I am finishing the fourth year of Kirov State University, faculty of computer science and computer engineering. With transfer and accommodation, SberSeasons helped me.

    After defending my diploma, my team at Sberbank is waiting for me. There are six of us in all, and all help each other. If you want, you can even pump up skills well in a couple of months and discover a lot of new things. For example, one day I was faced with tasks for which the capabilities of artificial intelligence were required. As a result, he began to study machine learning, and now I continue to do it on my own. I had to deal with bigdata, distributed computing, mastered Ktop. But the focus remained on Java programming, as I wanted. Many criticize this language, but it is very close to me. Isn't it cool to control and manage developer content using algorithms and a ton of fancy resources? I also like that in Java you cannot do without a general understanding of data structures. And I love this topic.

    Practice has shown that a banking Java programmer needs to be well versed in algorithms, databases, and distributed computing, understand version control systems, and understand the industrial structure of repositories. I already knew a lot, but, for example, Big Data had to be mastered directly in practice. All this requires constant training.

    By the way, I recommend Thomas Cormen's book Algorithms: Construction and Analysis on this topic. There he very sensibly explains how a hash, tree search, etc. works. In general, the main thing is not to focus on one information resource. Take different sources - and you will succeed.

    Natalya Massarskaya - risk modeling

    - I graduated from PhysTech, now I'm studying economics at NES. Prior to this, I already managed to undergo a month-long practice at another bank. This background perfectly pulled on an internship in the risk department in the back office. At SberSeasons, I was engaged in data processing for two months, created a model for urgent withdrawal of deposits of legal entities.

    Our entire department is engaged in similar patterns of customer behavior in terms of global impact on the business and risks of the bank: we assess interest, currency, liquidity risks and so on. I mainly worked on my project in tandem with the leader, but sometimes other team members joined in. If there are no restrictions on access to trade secrets, trainees can work on projects along with the rest.

    I liked the department, I would like to work here after graduate school. Until I returned to study, not enough time. But they promised me to choose a convenient part-time schedule, then I can return to the team again.

    In general, Sberbank turned out to be a surprisingly flexible organization. There is no minute-by-minute control by the management; you can easily take leave or come back later. There are, of course, differences from small companies, their own difficulties, but on the whole, everyone was pleased.

    My mentor believes that working in Risks requires logical thinking, knowledge of probability theory, statistics, the ability to build mathematical models and program. Personally, the standard set of hard skills helped me: Excel, Word, knowledge of econometrics. For two months in the department, I learned a lot of new things - for example, I learned how to compose queries in SQL and upload data. But in “Risks”, not only the “technical” part is important - mathematical education, knowledge of probability theory, statistics, data processing. It requires a common understanding of the economy and the banking business as a whole. It is necessary to replenish the theoretical base, to read more. Specifically for risks, I would advise to start the “Options, Futures and Other Derivative Financial Instruments” by John C. Hall.

    Dmitry Rudenko - data analysis

    - I am now in my fourth year at the VMK Moscow State University, I am studying an additional program for data analysis. I heard about SberSeasons at a lecture, I decided to try myself in this area. Filled out the questionnaire, passed the tests. The last interview was with my supervisor. He introduced me to the course, told me what to do in the department of applied data.

    The schedule in the department is flexible, the atmosphere in the team without unnecessary formalities. Our primary goal was customer identification by transaction. Three more interns worked on this project. Together we tried to identify signs on the basis of which artificial intelligence could produce the highest quality data. In parallel, they mastered new methods and tools used by professionals. Basically, they worked independently: they themselves set goals, set goals. Curators answered questions, helped with advice.
    Data analytics is slightly different from other areas of IT. My specialty is not so much programming as analytics: it takes much more time to think than to write code. If you are set to work in an application data center, you will have to deal with the construction of features, preparing data aggregation, evaluating the quality of models and programming. Be sure to be able to write scripts that process data. Of the soft skills - perseverance, patience, a little wit, as well as the ability to see what really is. And not what I want to see.

    During the two months of the internship, I mastered SQL-like databases well - I consolidated theoretical knowledge in practice. I also received good everyday knowledge: I was convinced that everything needs to be carefully checked and that the first impression cannot be trusted. Now I work in the same part-time department, combine with studies. But already doing other tasks.

    Valery Sopin - analytics

    - SberSeasons is already my second internship. Now I'm interested in analytics, I wanted to practice machine learning in finance. Sberbank attracted large volumes of information, funds and resources, as well as the fact that machine learning is still not very developed in the bank and there is something to work on.

    So I ended up in the department of analytical expertise. There were 8 of us, all working on the use of machine learning to identify fraudulent transactions. They brought up a problem for discussion, together came up with a solution, checked, worked on implementation. I had two leaders, very intelligent specialists. When setting tasks, my mathematical background was always taken into account.

    In addition to mathematical education, work in this area requires minimal algorithmic training: graph algorithms, conventional algorithms, parallel programming, statistics and probability theory; knowledge of Python, Scale, Hadoop. I had experience with machine learning - not much, but that was enough.

    When I was an intern in another company, I was trying to understand whether programming suits me or not. It turned out that no - programming is more of a technical thing, rather than creative or research. And the analyst’s job is to come up with, decide what and how can be done. It is necessary to study the experience of colleagues, combine different approaches, create something of their own. It suits me better.

    At SberSeasons, I learned not only about the features of the work of the analytical examination department. We were told in detail about the work of other departments, about the structure of the company. We had access to a virtual library; at lectures and trainings, heads of other departments tried to interest us. And rightly so: you need to understand a bit about everything in order to choose the most suitable.

    Learn more about internships here.

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