Junior Data Scientist

Professional experience is not mandatory. We value evidence of learning and practical curiosity. Relevant examples may include a university assignment, thesis, capstone project, Kaggle-style challenge, research activity, open-source contribution, or a small personal project.

What you can expect:

• A supportive onboarding and clear priorities;
• Mentoring and regular feedback from experienced Data Scientists;
• Hands-on exposure to real business problems, enterprise data, and collaborative AI delivery;
• Opportunities to strengthen technical, communication, and stakeholder-management skills;
• An inclusive team environment where questions, learning, and knowledge sharing are encouraged.

We are committed to fostering a workplace where everyone feels safe, respected, and valued. All kind of applications are welcome.

Ready to drive with Continental? Take the first step and fill in the online application.

At Continental Tires, we aim to make analytical excellence an integral part of the processes that create business value. Together with domain experts from our business units, we identify challenges that can be addressed through data, Advanced Analytics, and Artificial Intelligence.

As a Junior Data Scientist, you will join our global Data Science team through a temporary replacement. This role is designed as an entry point for someone who has built a foundation in data science through university studies, academic projects, a thesis, internships, or personal projects, and who is motivated to learn in a professional environment.

What you will do:

• Contribute to Data Science projects with guidance from experienced colleagues;
• Work with domain experts to understand business questions and translate them into clear analytical tasks;
• Explore, clean and prepare data from different sources for analysis and modeling;
• Develop and evaluate initial statistical or machine learning models using Python;
• Create clear visualizations and communicate findings to technical and non-technical stakeholders;
• Document datasets, experiments, assumptions, results and reusable code;
• Use Git and follow the team’s coding, review, quality, security and compliance practices;
• Participate in team ceremonies, knowledge-sharing sessions and peer reviews;
• Support the handover and continuity of ongoing activities.

• University degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, Physics, Economics, or a comparable quantitative field;
• Contact with data science topics through university coursework, a thesis, academic projects, internships, student organizations, competitions, or a personal portfolio;
• Basic programming knowledge in Python and familiarity with common data analysis libraries such as pandas, NumPy, scikit-learn, or equivalent tools;
• Basic understanding of statistics, data preparation, model training, validation and performance metrics;
• Ability to present an analysis clearly, explain choices and learn from feedback;
• Curiosity, structured problem-solving, reliability and a collaborative mindset;
• Good English communication skills, written and spoken.