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IT Internship

Data Science

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who is it for

The program is designed for students, graduates, or individuals at the beginning of their careers who wish to specialize in Data Science and build a solid foundation for a career in IT.

project impact

Through this internship, we actively contribute to the development of the IT ecosystem, support practical education, and create a framework for professional growth for young specialists, aligned with the real demands of the industry.

about the program

The IT Internship project is an initiative dedicated to training and developing the next generation of IT specialists, designed to provide participants with both solid theoretical knowledge and hands-on practical experience through real projects.

The program focuses on building competencies in the following key areas: Data Science

What the program offers

A structured learning program with theoretical and practical modules
Work on case studies and real projects, guided by experienced mentors
Development of technical skills and soft skills (teamwork, communication, analytical thinking)
Continuous feedback and evaluation throughout the program

See available domain

Profile / Role Title: Data Science

Role Purpose

To support the Data Science team in data collection, cleaning, exploration, and modeling, contributing to the delivery of insights and model prototypes (under mentor guidance) for priority business use cases.

Key Responsibilities:

Collecting, cleaning, and preparing datasets (data wrangling) from internal and external sources;
Conducting exploratory data analysis (EDA), identifying patterns, and formulating actionable conclusions;
Implementing basic models (regression / classification / clustering) and evaluating their performance;
Developing visualizations and reports (notebooks / simple dashboards) to communicate results;
Documenting steps, hypotheses, and outcomes; ensuring reproducibility (clean code, structured repository);
Collaborating with mentors and relevant teams (BI / Engineering / Product) to understand business requirements.

Required Technical Skills:

Python (pandas, numpy, scikit-learn) and experience working in Jupyter;
Basic to intermediate SQL (SELECT, JOIN, GROUP BY);
Fundamental knowledge of statistics (distributions, correlation, hypothesis testing — introductory level);
Data visualization tools (matplotlib / seaborn / plotly);
Basic version control knowledge (Git).

Required Behavioral Competencies:

Analytical thinking and attention to detail;
Curiosity, fast learning ability, and openness to feedback;
Clear communication skills (ability to explain findings to non-technical audiences);
Proactivity and ownership of small but complete tasks;
Teamwork and discipline in documentation and organization.

Desired Experience Level:

3rd–4th year undergraduate or Master’s student (Computer Science, Mathematics, Statistics, Economics, Engineering, etc.) or career transition candidate with relevant projects;
Practical experience: 1–3 academic projects / personal portfolio (GitHub) or participation in competitions (e.g., Kaggle) — considered an advantage.