Munich Thesis Causal ML for Prescriptive Decision Support in Automotive Goodwill (fmx) BY 80809
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What you'll do
- Support the literature review on state-of-the-art methods in Causal Machine Learning, Uplift Modeling, and causal inference.
- Help analyze available goodwill, customer, vehicle, and loyalty data.
- Support the development of a causal DAG and the identification of confounders such as vehicle model, service dealer, and customer history.
- Assist in developing and comparing Causal ML methods such as Meta-Learners, Causal Forests, Double Machine Learning, and Uplift Models.
- Contribute to evaluating treatment effect estimates and resulting goodwill allocation policies.
- Support the analysis of heterogeneous treatment effects across customer, vehicle, and repair segments.
- Help develop a decision-support framework for optimizing goodwill allocation.
What they're looking for
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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.
What awaits you?
- 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.
What should you bring along?
- 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.
Would you like to help shape goodwill decisions in a data-driven and impactful way using Causal Machine Learning? Then Apply now!
What do we offer?
- Comprehensive mentoring & onboarding.
- Personal & professional development.
- Flexible working hours.
- Mobile work.
- Attractive & fair compensation.
- Apartments for students (subject to availability & only at the Munich location).
- And much more, see bmw.jobs/waswirbieten
Start date: Earliest start date 19.10.2026
Duration: 6 Month
Working hours: Full-time
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We at the BMW Group place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experiences, and skills of the applicants. More about this here.
Company
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