Job Description
About the Internship
Join Uber's Team in Krakow and be a part of our journey to establish a cutting-edge Technology Hub.
As a Data Science Intern, you'll participate in designing new data-driven solutions. You will work on projects of varying sizes and time-horizons with the support of other Data Scientists, utilizing statistical modeling, causal inference methods and machine learning techniques.
This internship offers an excellent opportunity to enhance your analytical skills, sharpen your coding abilities, and work on impactful projects of varying areas such as: marketplace, compliance, fraud or expansion. Furthermore, it may serve as a stepping stone to exciting future opportunities within Uber.
About the Team
Located in Krakow, our team centralizes work for Uber Rides & Uber Eats, covering mega-regions like the United States & Canada, as well as Europe, Middle East, and Africa. Our mission statement is: backing creativity with operational efficiency.
We help Uber scale and gain efficiency by performing work around 3 main pillars:
- Data Science (modeling, causal inference, experimentation, machine learning)
- Analytics & Automation (query, deepdive, self-operating dash., process automation)
- Business Processes (design, execution, enhancement, optimization)
What You’ll Do
- Support Data Scientists, Data Analysts and Automation Specialists in building, evaluating and implementing scalable data-driven solutions
- Formulate business questions into testable hypotheses and experimental designs
- Develop and improve models using various advanced analytical methods
- Communicate findings to Stakeholders, adapting your communication to a variety of audiences
Basic Qualifications:
- Written and spoken fluency in English
- Currently enrolled in a master's degree program in STEM - Computer Science
- Availability to follow the internship full-time, for 3 months, starting 2 February 2026
- Working rights in Poland (No sponsorship available)
Preferred Qualifications:
- Good knowledge in Python and SQL
- Familiarity with causal inference, experimentation, statistical modeling, common ML methods, frameworks, tools & libraries (Scipy, Scikit-learn, PyTorch, Tensorflow, etc.)
- Familiarity with data visualization, GSheets, Looker Studio, would be a plus
