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Regular version of the site

Career Opportunities

The introduction and dissemination of new digital technologies, the penetration of the Internet and social media into the lives of a large number of people leads to the creation of a large number of 'digital traces', which, turning into 'big data', make it possible to study various phenomena and processes with unprecedented breadth, depth and scale. With the exponential growth of the volume of information, specialists in the field of data analysis are becoming indispensable for various areas of public activity - business, management, science and education.

There are already many vacancies in the field of data analytics available on the labor market. There is every reason to say that in the coming years the field of data science will develop dynamically, and the search for interesting projects and work will become more competitive, and employers will become more demanding of the competencies of applicants.

In 2012, Thomas H. Davenport and his colleagues published the article 'Data Scientist: Sexiest Job of the 21st Century,' in which they named the data analyst profession as the most attractive job of the 21st century. In 2022, a continuation of this article was published entitled 'Is Data Scientist Still the Sexiest Job of the 21st Century?', in which the authors noted that it is important not only to be able to build models, but also to load the necessary data into them, as well as manage the operation of systems, based on the goals set by the business – that is, to be well versed in the procedural issues of conducting applied research.

Companies and organizations of all sizes in different industries have a demand for specialists who can manage data flows and find valuable information in them. Specialists in this field must have a good knowledge of statistics and have knowledge in the relevant subject area. Knowledge of statistical methods is enhanced by skills from computer science.

The request for specialists in the field of data analysis is relevant not only for companies, but is also naturally included in the national strategic agenda for technological development (national projects 'Data Economy' and 'Artificial Intelligence'). In particular, it is the national project 'Data Economy' that confirms and updates the demand for specialists in the field of statistics and data analysis. It is important to be able to plan the economic development of individual industries, regions and cities, as well as to effectively and proactively structure the work of any organization to achieve results as quickly as possible.

A modern specialist in the field of data analytics must be able to work with large and complex data sets, be well versed in the methods of statistical data analysis and apply in practice the latest advances in the field of statistics, understand all stages of research, as well as be able to program and understand the trends in the development of artificial intelligence.

The master's programme 'Data Analytics and Applied Statistics' allows students to acquire the skills required of data analysts by employers from various fields: the banking sector, insurance companies, IT and telecommunications companies, public and private analytical and consulting centers, scientific organizations. Applied statistics is the science of applying statistical methods of analysis to solve applied problems in various fields using computer data processing, which is the basis of modern data analysis. Our programme emphasizes methods used in the social sciences.

Students in the programme can choose from two specializations – computational social and network sciences or applied statistics and data sciences – and shape their studies to suit their professional interests. To help programme graduates become more competitive in their field, both tracks provide specific internship opportunities.

Students who choose the applied statistics and data science track have the opportunity to complete internships in companies where their analytical skills are needed. The knowledge gained in the programme allows our students to work in leading Russian and international companies during their studies: Volkswagen Group, Thomson Reuters, Synergy Research Group, Ingosstrakh, CROC, Hephaestus Pension Fund.

Graduates of this track can work as data analysts or product analysts in a variety of areas, solving both research problems and applied problems in product and process management within their organizations.

Students who choose the computational social and network sciences track can undertake internships at the International Laboratory for Applied Network Research and other departments of the National Research University Higher School of Economics, as well as in partner consulting and research organizations. Students have the opportunity to go on an internship as part of internal university competitions.

Graduates of this track can work in the research industry in the field of applied social research, applying advanced methods to study various social phenomena and processes, as well as in an academic environment.

Since the programme pays special attention to design issues and the specifics of conducting social research, academically career-oriented graduates can enroll in PhD programs or graduate school.

Those who want to learn more about their future profession will be interested in reading an interview with the first academic supervisor of the master’s programme 'Applied Statistics with Network Analysis' Valentina Kuskova, as well as watching an interview with the current academic supervisor Ivan Klimov.

Success Stories

Elena Beylina

Operational Analysis and Reporting Development Specialist: Japan Tobacco International

Dmitry Donetskov

Data Scientist in Yandex

Ekaterina Melianova

Data Scientist in World Bank

Alexander Alimov

Analytical consultant: PricewaterhouseCoopers, PwC

Dina Yakovleva

Senior Content Analyst in Thomson Reuters

Elizaveta Chernenko

Ph.D programme Oxford Internet Institute (DPhil in Social Data Science)

Stepan Zaretskiy

Ph.D programme of Inter-university Center for Social Science Theory and Methodology (ICS) and the Department of Sociology of the University of Groningen

Gregory Khvatsky

HSE doctoral programme in Sociology

Mikhail Bogdanov

Junior Research Fellow HSE University