How I spent SberSeasons: four stories about different specialties
They say that working in a bank is boring, and an internship is monotonous - you sit and sort out papers. We disagree with that. And, hopefully, the participants of SberSeasons disagree with this - a paid internship for future graduates in technical and mathematical specialties. Let's check it out and distract a little from the work of four interns from different departments - a server programmer, a risk model designer, a data scientist and an analytical expert.
- I dreamed of working as a server programmer of the class from the seventh. And not disappointed. The work was fascinating, and I even spent the weekend in the office. I attended all the extra classes that we had twice a month, in other areas and topics, for example, Docker.
I came for an internship from Kirov. Now I am finishing the fourth year at the Kirov State University, the faculty of computer science and computing. SberSeasons helped me with the transfer and accommodation.
After defending my diploma, my team in Sberbank is waiting for me. There are six of us, and all help each other. If there is a desire, you can even well pump skills and discover a lot of new things in a couple of months. For example, once I ran into tasks that required the capabilities of artificial intelligence. As a result, I began to study machine learning, now I continue on my own. Had to do bigdata, distributed computing, mastered Ktop. But the focus remained on Java programming, as I wanted. Many people criticize this language, but it is very close to me. Isn't it cool to control and manage developer content with algorithms and masses of distributions? I also like the fact that Java cannot do without a common understanding of data structures. And I love this topic.
')
Practice has shown that a Java banking programmer needs to be well-versed in algorithms, databases and distributed computing, understand version control systems and understand the industrial design 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, on this topic I recommend the book by Thomas Cormen "Algorithms: construction and analysis." There, he very sensibly explains how the hash works, searching through trees, etc. In general, the main thing is not to focus on one information resource. Take different sources - and all you get.
- I graduated from the Physical and Technical University, now I study economics at NES. Before that, she had already completed a month's practice in another bank. This background perfectly pulled on an internship in the risk department in the back office. At SberSeasons, I spent two months processing data, creating a model of urgent withdrawal of deposits of legal entities.
Such models of customer behavior in terms of global influence on the business and risks of the bank are handled by our entire department: we evaluate interest, currency, liquidity risks, and so on. I worked on my project, mainly in tandem with the manager, but sometimes other team members joined. If there are no restrictions on access to trade secrets, interns can work in projects along with the rest.
I liked the department, I would like to work here after the magistracy. While I returned to school, there is not enough time. But I have already promised to find a convenient schedule for part-time, then I will be able to return to the team again.
In general, Sberbank was surprisingly flexible organization. No constant control by the management, you can easily take time off or come back later. There are, of course, differences from small companies, their difficulties, but on the whole, everything pleased us.
My mentor believes that working in “Risks” requires logical thinking, knowledge of the theory of probability, statistics, the ability to build mathematical models and to program. Personally, I was helped by a standard set of hard skills: Excel, Word, knowledge of econometrics. In two months in the department I mastered a lot of new things - for example, I learned how to write queries in SQL and upload data. But in “Risks” not only the “technical” part is important - mathematical education, knowledge of the theory of probability, statistics, data processing. It requires a general understanding of the economy and the banking business as a whole. It is necessary to replenish the theoretical base, read more. Specifically on risks, I would advise to start “Options, Futures and Other Derivative Financial Instruments” by John C. Hull.
- I am now in my fourth year at the Moscow State University Moscow University; I am studying on an additional program for data analysis. I heard about SberSeasons at the lecture, I decided to try myself in this area. Filled in the form, 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 application data.
The schedule in the department is flexible, the team atmosphere without unnecessary formalities. Our main task was to identify customers by transaction. Three more trainees worked on this project. Together we tried to identify the signs on the basis of which artificial intelligence could produce the most qualitative data. At the same time, they mastered new methods and tools used by professionals. Basically, we worked independently: we set goals for ourselves, set goals. Curators answered questions, helped with advice.
Data analytics is slightly different from other IT areas. My specialty is not so much programming as analytics: it takes much more time to think than to write code. If you are set up to work in the application data center, you will have to deal with the construction of features, the preparation of data aggregation, the assessment of model quality and programming. Be sure to be able to write scripts that process data. From soft-skills - perseverance, patience, a little wit, as well as the ability to see what really is. And not what you want to see.
For two months of internship, I have mastered SQL-like databases well - consolidated my theoretical knowledge in practice. I also received quite good everyday knowledge: I made sure that everything had to be carefully checked and that the first impression could not be believed. Now I work in the same part-time department, combining with studies. But already doing other tasks.
- SberSeasons is already my second internship. Now I am interested in analytics, I wanted to practice machine learning in the field of finance. Sberbank has attracted large amounts of information, funds and resources, as well as the fact that the bank has not yet developed machine learning and there is a lot of work to be done.
So I ended up in the department of analytical expertise. There were 8 of us, all working on using machine learning to identify fraudulent transactions. They brought up some problem for discussion, together came up with a solution, checked, worked on the implementation. I had two managers, very intelligent experts. When setting the problem, my mathematical background was always taken into account.
In addition to mathematical education, a minimum algorithmic preparation is needed for work in this field: algorithms on graphs, ordinary 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 at another company, I tried to understand whether programming suits me or not. It turned out that there is no - programming is more a technical thing, not creative or research. And the job of an analyst is to invent, decide what and how to do. It is necessary to study the experience of colleagues, combine different approaches, create something of your own. It suits me more.
At SberSeasons, I learned not only about the features of the work of the department of analytical expertise. We were told in detail about the work of other divisions, about the structure of the company. We had access to the virtual library; at lectures and trainings, we were interested in the heads of other departments. And rightly so: you need to understand a bit about everything in order to choose the most suitable one.
Read more about the internship here.
Nikita Batrukhin - Java Programming
- I dreamed of working as a server programmer of the class from the seventh. And not disappointed. The work was fascinating, and I even spent the weekend in the office. I attended all the extra classes that we had twice a month, in other areas and topics, for example, Docker.
I came for an internship from Kirov. Now I am finishing the fourth year at the Kirov State University, the faculty of computer science and computing. SberSeasons helped me with the transfer and accommodation.
After defending my diploma, my team in Sberbank is waiting for me. There are six of us, and all help each other. If there is a desire, you can even well pump skills and discover a lot of new things in a couple of months. For example, once I ran into tasks that required the capabilities of artificial intelligence. As a result, I began to study machine learning, now I continue on my own. Had to do bigdata, distributed computing, mastered Ktop. But the focus remained on Java programming, as I wanted. Many people criticize this language, but it is very close to me. Isn't it cool to control and manage developer content with algorithms and masses of distributions? I also like the fact that Java cannot do without a common understanding of data structures. And I love this topic.
')
Practice has shown that a Java banking programmer needs to be well-versed in algorithms, databases and distributed computing, understand version control systems and understand the industrial design 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, on this topic I recommend the book by Thomas Cormen "Algorithms: construction and analysis." There, he very sensibly explains how the hash works, searching through trees, etc. In general, the main thing is not to focus on one information resource. Take different sources - and all you get.
Natalya Massarskaya - risk modeling
- I graduated from the Physical and Technical University, now I study economics at NES. Before that, she had already completed a month's practice in another bank. This background perfectly pulled on an internship in the risk department in the back office. At SberSeasons, I spent two months processing data, creating a model of urgent withdrawal of deposits of legal entities.
Such models of customer behavior in terms of global influence on the business and risks of the bank are handled by our entire department: we evaluate interest, currency, liquidity risks, and so on. I worked on my project, mainly in tandem with the manager, but sometimes other team members joined. If there are no restrictions on access to trade secrets, interns can work in projects along with the rest.
I liked the department, I would like to work here after the magistracy. While I returned to school, there is not enough time. But I have already promised to find a convenient schedule for part-time, then I will be able to return to the team again.
In general, Sberbank was surprisingly flexible organization. No constant control by the management, you can easily take time off or come back later. There are, of course, differences from small companies, their difficulties, but on the whole, everything pleased us.
My mentor believes that working in “Risks” requires logical thinking, knowledge of the theory of probability, statistics, the ability to build mathematical models and to program. Personally, I was helped by a standard set of hard skills: Excel, Word, knowledge of econometrics. In two months in the department I mastered a lot of new things - for example, I learned how to write queries in SQL and upload data. But in “Risks” not only the “technical” part is important - mathematical education, knowledge of the theory of probability, statistics, data processing. It requires a general understanding of the economy and the banking business as a whole. It is necessary to replenish the theoretical base, read more. Specifically on risks, I would advise to start “Options, Futures and Other Derivative Financial Instruments” by John C. Hull.
Dmitry Rudenko - data analysis
- I am now in my fourth year at the Moscow State University Moscow University; I am studying on an additional program for data analysis. I heard about SberSeasons at the lecture, I decided to try myself in this area. Filled in the form, 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 application data.
The schedule in the department is flexible, the team atmosphere without unnecessary formalities. Our main task was to identify customers by transaction. Three more trainees worked on this project. Together we tried to identify the signs on the basis of which artificial intelligence could produce the most qualitative data. At the same time, they mastered new methods and tools used by professionals. Basically, we worked independently: we set goals for ourselves, set goals. Curators answered questions, helped with advice.
Data analytics is slightly different from other IT areas. My specialty is not so much programming as analytics: it takes much more time to think than to write code. If you are set up to work in the application data center, you will have to deal with the construction of features, the preparation of data aggregation, the assessment of model quality and programming. Be sure to be able to write scripts that process data. From soft-skills - perseverance, patience, a little wit, as well as the ability to see what really is. And not what you want to see.
For two months of internship, I have mastered SQL-like databases well - consolidated my theoretical knowledge in practice. I also received quite good everyday knowledge: I made sure that everything had to be carefully checked and that the first impression could not be believed. Now I work in the same part-time department, combining with studies. But already doing other tasks.
Valery Sopin - analyst
- SberSeasons is already my second internship. Now I am interested in analytics, I wanted to practice machine learning in the field of finance. Sberbank has attracted large amounts of information, funds and resources, as well as the fact that the bank has not yet developed machine learning and there is a lot of work to be done.
So I ended up in the department of analytical expertise. There were 8 of us, all working on using machine learning to identify fraudulent transactions. They brought up some problem for discussion, together came up with a solution, checked, worked on the implementation. I had two managers, very intelligent experts. When setting the problem, my mathematical background was always taken into account.
In addition to mathematical education, a minimum algorithmic preparation is needed for work in this field: algorithms on graphs, ordinary 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 at another company, I tried to understand whether programming suits me or not. It turned out that there is no - programming is more a technical thing, not creative or research. And the job of an analyst is to invent, decide what and how to do. It is necessary to study the experience of colleagues, combine different approaches, create something of your own. It suits me more.
At SberSeasons, I learned not only about the features of the work of the department of analytical expertise. We were told in detail about the work of other divisions, about the structure of the company. We had access to the virtual library; at lectures and trainings, we were interested in the heads of other departments. And rightly so: you need to understand a bit about everything in order to choose the most suitable one.
Read more about the internship here.
Source: https://habr.com/ru/post/352416/
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