Who is Data Scientist - the eyes of the employer

Ksenia Suvorova, Development Director of Fontanka.ru , and Andrei Miroshnichenko, coordinator of the offline Data Scientist program, specifically for the Netology blog, spoke about the Data Scientist profession from the employer: what kind of specialists are required by the market, what competencies are expected of them and how are they hired to work.

Now everything has turned out in such a way as once the story with product and project management: there are specialists in the market, they already have a well-established market value, there are vacancies, but not everyone knows who this is and why this person is at all business needs. Therefore, we decided to talk with Avito, the HR agency Spice IT and Storia.me to understand what the development of the profession really is.

A look at Avito from a direct employer perspective - says Alexander Golovin

“The demand for data science specialists is very great and will only continue to grow. However, there are also many opportunities for training: any person who understands that he does not have enough academic education can take courses and get the necessary base.

The question, rather, is who and why comes into the profession. At the interview, applicants say they are interested in machine learning, and when you start asking why, they answer: “It's fashionable.” And that’s it. Understanding how to apply knowledge, no.

But in business it’s not so. There is a problem for which it is necessary to find the optimal solution method. The specifics is that this solution is practicable. And this is probably the main problem that we face when selecting people.

Some applicants believe that it’s enough to come up with a beautiful algorithm, and the fact that it cannot be applied anywhere is the tenth thing.

Examination, in turn, can be divided. There are people who have worked in a field close to us - classifications and IT. They perfectly understand where and how to apply knowledge. People who come from another area — from banks, yesterday’s graduates, or who worked in the laboratory — are losing in this regard, but for us this is not an indicator. They go into deep learning, deep networks, trying to find something more complicated. Although in fact, a model that will work can be much simpler.

Skills can be divided into hard and software. As for hard: education is necessarily mathematical. The specialist must understand how mathematical models work.

They come to us, as a rule, from leading universities: Moscow Institute of Physics and Technology, Higher School of Economics, Moscow State University. Among the graduates of the latter, there is even a joke in the competition, of whom there are more in the company - those who have completed mechanics or military training.

There is also a conditional division into product analysts and ML analysts. The task of the first is to look for the possibility of improving the product, generating hypotheses about possible user problems and how to quickly solve them, and the second automates the solutions found by product analysts and tasks using various ML methods: personal recommendations, pricing, and so on.

We check basic skills on a test task. The department is large, it consists of several departments that support different systems. Therefore, each department has developed its own case, as close as possible to what to do in the future. When solving such a case, the candidate’s skills become obvious. After that, we look at the code and decide who to invite to the meeting.

About soft skills. This is the part that we always pay attention to in personal communication with the candidate. Since data science specialists are involved in cross-functional projects, it is very important for us that a person shares the company's values, can work in a team and build communication with colleagues. ”

Spice IT - from the position of an HR agency

“There are more and more Data Scientist jobs. Data is the most valuable product on the market. There will be no recession in the near future. There are already not enough specialists, especially when it comes to such vacancies as Head of Predictive Analytics or Lead / Chief data scientist. Candidates are busy on serious projects and do not want to quit what they started. Plus, these positions imply the presence of special qualities required by a company. It is easier with interns and joons: data science is starting to gain momentum, and many are happy to try their hand at this.

Professional competencies depend on the requirements set by the customer company. From the main we can distinguish: R, Python, Machine Learning, databases such as MSSQL, MySQL, Postgresql. Candidates for Data Scientists must be well versed in mathematics, statistics, and programming.

Jobs where soft skills are one of the key requirements are rare enough if this is not a leadership position. Due to the fact that there are not very many strong specialists in the market, the emphasis is on the technical component.

Of course, many companies would like to see in their ranks professionals with a proactive attitude, preparing presentations, composing beautiful reports and knowing how to establish contact with colleagues and management, but, again, in practice, most customers give up these requirements, preferring good technical experience to communication skills.

At the same time, companies are ready to train, accepting candidates as trainees or junior analysts. We are ready to watch the guys who lead projects on freelance or do something for themselves in order to gain experience. We have job vacancies at various levels where specialists with minimal experience or strong technical skills are needed. A lot of options, everyone can pick something up.

Experience with Data Scientist, former marketing director of Storia.me - Alina Gashinskaya

“At Storia, there were two big data specialists in one period. We hired them for specific tasks: it was necessary to work with predictive analysis in order to improve marketing indicators and correct the situation with a high churn rate. In addition, we wanted to build our own recommendation system inside the site without taking a ready-made solution for this.

It seems to me that Data Scientist should be able to work under the tasks of the product, but without basic skills, of course, nowhere. Languages ​​for collecting data, queries, processing information, working with databases, certain knowledge in statistics - this pool of skills at the output will be used for a specific task, and in any situation, Data Scientist must understand how he will solve this or that problem.

It makes no sense to hire a big data specialist to just sit in the office - it will be quite expensive. It is more profitable to hire a project if there is no third-party solution or if the product needs internal development.

Working with big data can be useful for UX, and for development, and for marketing. You need to see if a specialist of this format is really needed.

I would say that the future is really for working with big data, but with some reservations. It’s not enough to get the data, you still need to understand how they can be used. Data specialists can deal with many tasks, but usually they are nevertheless necessary for a large company - there are tasks and budgets for them.

For large companies in the face of stunted growth, a specialist in data science is an opportunity to find a new development path, a way to attract solvent customers. Working with Big Data is not something that a person can do, because our brain is simply not able to process such a quantity of data.

Now we are only developing an understanding of the necessary competencies and the image of a specialist in working with big data. This direction is too new for the Russian market and our realities. Our requirements for other specialists are not always correctly formulated, let alone a Data Scientist.

In addition, you must understand that working with big data gives results only in the long term and does not solve issues that must be resolved today or tomorrow. For example, you are planning a redesign in a year, and you want to make a full-fledged friendly interface. In this case, you hire a specialist in working with big data, he conducts A \ B-tests and predictive analysis. Such data is more accurate because it is machine learning that is error-free. And this is also more competent and wider opportunities for advertising companies, the target audience and its analysis. ”

Tips that an employer can give a Big Data Specialist

1. Courses, of course, are needed. But they must be layered on knowledge of programming languages: R or Python.
2. Without understanding the principles of machine learning, nowhere.
3. Math and statistics should be your friends.
4. Data is numbers, numbers, mathematical analysis. Therefore, make them your gods.
5. It is not enough to know the theory; one has to understand the practical component. Going to work in a business and thinking that an angel data hearth is needed is a mistake. There you need a solution to a specific problem.
6. In Russia, big data is, first of all, analytics. Therefore, if you want to do something else, then you will have to look for work longer.
7. You may come across a “taut” employer who will himself poorly understand the role of Data Science - this is normal. You are not a marketer, not a traffic manager, or even an analyst; you are something more.
8. The task of any Data Scientist is to process the data and provide the result. With the help of your knowledge and skills, drones fly around the world, taxis and cars without human control will soon be driving, neural networks are working, millions of advertising campaigns in Google and Yandex are being processed. You are priceless, but everything has a price and its name is salary. Appreciate yourself and success to you.

Read the full interview with Avito and Spice IT about Data Scientist in our next article .

From the editors

Netology courses on the topic:

• full-time course " Data Scientist ";
  
• online program " Big Data: the basics of working with large data sets. "