As a Data Scientist, you will work along the entire model life cycle guiding Data Products from concept to production. You will work in a team, where we try to fundamentally rethink business problems and find new innovative solutions using machine intelligence. Additionally, you will be engaged in close cooperation with other teams, working in cross-functional settings. As a team we love having fun together – hiking in the mountains, cracking hackathons, and enjoying with other teams.
Are you planning to become an expert in Data science area? We help you to start your professional career in assistance of our skilled engineers.
Become a part of our team!
Your profile:
- You are a student of min. 3rd year in some technical/IT faculty
- You are ambitious and want to learn new technologies
- You are ready to join our team first for 3 months long summer internships and stay with us after that to work for at least 32 hours/week
- You are ready to work with us regularly in our office in Warsaw
- You have good communication and English skills
- You are familiar with some of the technologies and working methods from our TechStack:
- - Python programming
- - Strong understanding of Object-Oriented Programming principles
- - Proficiency in SQL for database management
- - Solid foundation in statistical analysis
- - Familiarity with Data Processing, Data Visualization, and Machine Learning libraries
- - Experience in implementing modularized code for better maintainability
- - Git and Bitbucket
Our offer:
- Salary for 3 months long internship
- Onboarding trainings and dedicated buddy during internship program 6 days off (to take your breath during internship)
- 6 days off (to take your breath during internship)
- At the end of the program possibility for a job offer (min 32 hours/week) for the best interns
- Tasks from many different areas (depending on your experience and willingness to learn). Some examples of tasks are:
- - Performing in-depth exploratory data analysis for trend and pattern identification
- - Preprocessing data to ensure its quality and readiness for modeling
- - Assessing and enhancing model performance through metrics and validation techniques
- - Crafting data visualizations to clearly convey insights to stakeholders
- - Thoroughly documenting methods, outcomes, and best practices for future reference
How about?
