Thesis Causal ML for Prescriptive Decision Support in Automotive Goodwill

More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly novel solutions. That's why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table – and give you the opportunity to really show what you can do. Our team at the BMW Group focuses on the data-driven optimization of goodwill decisions. We rely on Causal Machine Learning and Uplift Modeling to enhance customer satisfaction, loyalty, and budget efficiency. As part of your internship, you will support us in developing new analytical methods.

• You will support the literature review on state-of-the-art methods in Causal Machine Learning, Uplift Modeling, and causal inference.
• Furthermore, you help analyze available goodwill, customer, vehicle, and loyalty data.
• In addition, you support the development of a causal DAG and the identification of confounders such as vehicle model, service dealer, and customer history.
• Moreover, you will assist in developing and comparing Causal ML methods such as Meta-Learners, Causal Forests, Double Machine Learning, and Uplift Models.
• Furthermore, you contribute to evaluating treatment effect estimates and resulting goodwill allocation policies.
• In addition, you support the analysis of heterogeneous treatment effects across customer, vehicle, and repair segments.
• Moreover, you help develop a decision-support framework for optimizing goodwill allocation.

• Studies in (business) informatics, data science, statistics, (business) mathematics or a related field.
• Strong analytical and problem-solving skills.
• Experience with Python and common machine learning libraries.
• Solid knowledge of statistics and machine learning.
• Interest in Causal ML, experimentation, and data-driven decision-making.
• Good German and English skills.