Data Scientist is one of the fastest growing specialties of the 21st century. Frost & Sullivan predicts that the big data analytics market will grow by an average of 35.9% per year over the next 10 years.
In this article, we'll look at how much money a data scientist can get (spoiler alert: a lot), what requirements are most often in vacancies, how to come to DS and where to develop. Ready? Go!
Who is a data scientist
A data scientist is engaged in the analysis of data arrays - Big Data. Using mathematical analysis and identifying patterns in the data, the data scientist creates models to solve specific business problems or problems.
In fact, the profession of data scientist is closely related to data analyst (Data Analyst or DA) and data engineer (Data Engineer or DE). So closely spaced that most grocery companies don't separate them at all. And the Data Scientist often takes over the DA and DE responsibilities.
But working with Big Data still has its own specifics and peculiarities. The distribution of working hours of data scientists can be expressed in a diagram:
Machine learning is an important part of the data scientist profession. Neural networks are becoming more and more popular for analyzing data sets, so a specialist must be able to work with them.
One of the main goals is to achieve a business result. After all, it is with the help of Data Science that predictive models are developed. For example, the behavior of users on the network, currency rates, stock prices and much, much more. It is data scientists who have developed YouTube recommendation algorithms and improve Google's search results.
What employers require from a data scientist
We analyzed over 400 vacancies for data scientist positions opened in October-November 2020.
A clear boundary between the specialties of data scientist, data analyst and data engineer exists only for IT companies and large corporations with large IT departments. Therefore, job vacancies for data scientists often come across tasks that are more appropriate for an analyst or data engineer.
During the analysis of vacancies, we have identified a number of skills and abilities that employers most often request from job seekers.
First, let's deal with hard skills
High math skills. Higher mathematics, probability theory, mathematical and applied statistics is a must-have for a Big Data analyst. Over 60% of vacancies directly indicate the need for good mathematical preparation or require a bachelor's or master's degree from a university in mathematics, engineering or information technology.
Python and libraries for data analysis and machine learning . Python is listed in 81% of jobs. Also, most often employers require knowledge of special libraries: TensorFlow, Keras, PyTorch, LightGBM, NumPy, SciPy, Pandas, sklearn.
In about a third of vacancies, employers indicate knowledge of Python and / or R. But specifically R (without Python) is rarely requested - only 12 vacancies out of 400. Other programming languages are required in about 3% of cases.
SQL . Databases are the backbone of DS. Therefore, skills in working with relational databases are needed in more than 73% of vacancies. NoSQL databases are less popular - they are needed less than 10% of the time.
Excel stands alone. Although it is not included in the stack of required skills, some companies build data analytics in it. Why this is so is not clear. Perhaps they are simply confusing the functions of data analyst and data scientist.
Data visualization systems... As a data scientist, it is important not only to create working models and forecasts, but also to be able to present this to management. It is desirable in a clear and simple way. Most companies (55%) simply indicate "data visualization systems" - for them it is absolutely not important which ones the applicant will own. But among the most popular are only three - Tableau, Metabase, and Power BI.
Machine learning and Deep learning . Machine learning and deep learning are important. Almost 40% of companies separately emphasize that the applicant needs to understand at least in general terms how it all works and how to use it in business.
Many companies point out the need for knowledge of technical Englishwith a level not lower than Intermediate. Conversational skills are often not needed, but you will have to read technical documentation. Moreover, almost all new developments in Data Science are published in English, so you need to understand it at least at an intermediate level.
In terms of hard skills in general, that's all. Well, or almost everything, because other options are found in less than 10% of vacancies.
Soft skills
In general, they are quite expected. Here are the most common ones that companies ask for:
- Analytical mind;
- High communication skills;
- Ability to take initiative;
- Attention to details;
- Responsibility and independence;
- Multitasking.
Data Scientist salary and benefits
Now the most exciting thing is the salary and bonuses. As of 11/13/2020, 325 vacancies are open in Russia for the position of Data Scientist. Of these, 175 are in Moscow (54%), 54 are in St. Petersburg (16%). The remaining 30% are roughly evenly distributed across regional centers.
Important! Many companies offer telecommuting with staffing. In a quarantine environment, this is beneficial. That is, if you wish, you can easily find a Junior Data Scientist vacancy in Moscow, but at the same time being in Voronezh or Tver.
True, Middle and Senior specialists are often needed offline. They receive a greater level of responsibility, which is why companies still prefer to work with them in the office.
The vast majority of vacancies are posted by IT companies (78%). Data Scientists are also needed in the banking sector (10%) and educational projects (8%). The rest of the industries occupy no more than 4% of vacancies.
The main problem with the sample is that a very small number of companies indicate a salary bracket. Or at least somehow orient the applicant to the level of material compensation.
Only 20% of companies openly indicate the level of salaries. The rest are content with vague wordings like "salary on the market" or "discussed at an interview."
In open vacancies, the level of pay is very decent. In Moscow, a specialist with about 2 years of experience in DS can count on an average salary of about 200,000.
Senior Data Scientist with at least 5 years of experience and a wide stack of competencies in large companies can receive up to 500,000 rubles per month. And it's not a joke. There are such vacancies and people are quite hired for them.
A young Junior with no experience or with experience of up to a year can count on a salary of up to 100,000.
Additional benefits are also quite serious. We will not take into account the standard "coffee, tea, cookies" - they are in almost every vacancy.
Many companies offer flexible working hours in addition to online work. The possibility of further training and advanced training at the expense of the company is also popular - about a third of companies agree to support an employee if he wants to develop in his specialty.
The most valuable bonus, in our opinion, is VHI. Almost all major companies offer full health insurance, and many include dentistry. Moscow medicine is expensive, so treatment under insurance in good clinics is a great bonus for the company's employees.
The St. Petersburg level of salaries is slightly behind the Moscow level. In the northern capital, Junior Data Scientist can count on a salary of 45,000 rubles or more. The maximum for a specialist with no experience or with less than a year's experience is 90,000 rubles. Nevertheless, there are only a few such vacancies.
On average, a specialist with a normal knowledge base and experience of at least 2 years receives a salary of about 150,000 rubles. But leading data scientists and team leaders are a separate category. The salary there is the same as in Moscow - up to 500,000 rubles a month.
Additional benefits are almost the same as in Moscow, except that a much smaller number of companies offer full medical insurance. Although purely subjective - there are more non-trivial and interesting bonuses.
Some offer a daily darts tournament at lunchtime, others a PlayStation, and still others offer collective entertainment and varied team building.
The situation in other regions of Russia is much worse. And the level of salaries is strikingly different from Moscow and even St. Petersburg.
An inexperienced specialist can count on a salary of 20,000 to 40,000 rubles. Moreover, in such vacancies, the boundaries of professions are most blurred. Often, under the name of the vacancy "Data Scientist", they are looking for an analyst who will analyze all the data in the company at once. Some HRs even manage to advertise vacancies like "Python Developer - Data Scientist". A kind of "jack of all trades" in analytics, and not only.
In general, a specialist with experience can count on a salary of about 100,000-120,000 rubles. The maximum rate in regional centers is 180,000 rubles. And there are really few such vacancies.
Where to come to the profession and where to grow data analytics
A data scientist really needs a good background in math. He works with complex mathematical models, so at least an average level of higher mathematics, probability theory and statistics should be required.
This is one of the main reasons why few people get into the field of Data Science from scratch. It is possible to improve the necessary mathematical basis even without university preparation, but it is difficult.
Most often, Data Science is leaving data analytics. Data Analyst already has almost all the necessary competencies and knowledge of special tools. He only needs to pull up mathematics and more applied areas like machine learning and deep learning.
The road to Data Science is also open for Python developers. Most companies require knowledge of Python as a programming language for analytics. If you have any mathematical background and knowledge of SQL, that's fine.
Now the entire field of Data Science is in a stage of active growth, so the prospects are excellent. A high-level professional can grow both vertically and horizontally. That is, you can grow into the head of the Data Science department, who, in fact, is the vice president of the company - his influence on the strategic planning of all activities is simply enormous. Or, you can try yourself in almost any other branch of analytics - business analytics, product analytics, software analytics, systems analytics.
Some data scientists are going back to Python development, but there aren't too many. You can try more interesting options - for example, an artificial intelligence architecture for computer games. There are enough opportunities - check it yourself:
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