GOODYEAR. MORE DRIVEN.

Goodyear Tyres have an exciting opportunity in Luxembourg for an internship within Data Science – Image Processing. This internship will be a duration of 6 months, beginning in early 2025. To apply, you must be currently enrolled in university.

THE OPPORTUNITY:

The candidate will join a multicultural data science team at Goodyear. The project portfolio includes data science and AI projects for tire performance prediction, tire intelligence solutions and business operations.
We are currently looking for an Intern in Data Science to join our team in Luxembourg. This role offers a unique opportunity to apply cutting-edge techniques in data science and image processing to real-world challenges.
As a Data Science Intern, you will focus on developing and optimizing ML/DL models for image processing tasks related to tire detection, classification, and damage assessment. The goal of this project is to extract valuable information from images of tires and accurately identify any damages or anomalies.

EDUCATION

The student must be enrolled in the last year of a bachelor’s or master’s degree in data science or mathematics.

SKILLS

• Experience with coding in Python.
• Data Science principles, statistics and applied mathematics.
• Some experience with common data science libraries (scikit-learn, numpy, panda, …).
• Basic experience with source control system (Git), Linux and High-Performance Computing would be an asset.
• Strong verbal and written communication skills.
• Willingness to work with a diverse team, share ideas, and collaborate on solutions.
  Goodyear is one of the world’s largest tire companies. It employs about 71,000 people and manufactures its products in 55 facilities in 22 countries around the world. Its two Innovation Centers in Akron, Ohio, and Colmar-Berg, Luxembourg, strive to develop state-of-the-art products and services that set the technology and performance standard for the industry. For more information about Goodyear and its products, go to www.goodyear.com/corporate

• Review the state-of-art literature.
• Perform exploratory data analysis.
• Data Handling and Preprocessing.
• Build, train and validate a ML/DL models and support models industrialization.
• Creation of documentation, presentation of results, transfer of knowledge to one permanent team member.