Internship in Data Science - Causal inference methods and tools (m/f)

Goodyear. More Driven.

The Opportunity:
The objective of this internship is to review and evaluate existing techniques and approaches related to causal machine learning models and algorithms. The student needs to determine pros and cons, difficulties of these methods and select preferred approaches.
After the theoretical part, the student is required to develop tools and packages and apply them on internal/external datasets. 

Responsibilities:

• Specific objectives:
• Review literature, bibliography, and training materials on causality and summarize findings.
• Benchmark different packages using internal/external dataset and determine pros/cons.
• Implement into a python library.
• Run a full case and validate outcomes with data science and subject matter experts against internal knowledge or guidelines.

To join our team, you will need:

Education:
Master Student (last year before graduation):  Statistics, Data Science, Applied Mathematics.

Languages:
English is a must.

Skills & Qualifications:  

• Technical competencies required:
• Python programming (proved experience in academic projects)
• Basic data science
• Inferential statistics, Bayesian statistics
• Hypothesis testing
• Elements of graph theory
• Familiar with source control systems (Git), high-performance computing clusters (HPC), IDLE, …
• Team player, curious, able to understand math behind algorithms.

You must be available for 6 months & be enrolled in university.
Ideal starting date : Q1 2024
 

Goodyear is one of the world’s largest tire companies. It employs about 72,000 people and manufactures its products in 57 facilities in 23 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.