Thesis Causal ML for Prescriptive Decision Support in Automotive Goodwill (f/m/x)
Required skills
Job description
BMW Group posted this role. Below, we break down what it means for a working student in Munich: your weekly hours, take-home pay and visa limits.
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Description provided by BMW Group
Some It Works. Some Changes What's Possible.
SHARE YOUR PASSION.
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.
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
You can find helpful tips on your application and the application process here.
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.
Thesis Causal ML for Prescriptive Decision Support in Automotive Goodwill (f/m/x)
Working student essentials
What this Tech working student role in Munich means for you: the weekly-hours rules, the social-contribution perks, and what international students should check before applying.
Weekly hours
Working students may work up to 20 hours a week during the semester and full-time during the breaks. Staying within this keeps your student status and the Werkstudent benefits.
Working student rulesSocial contributions
Under the Werkstudentenprivileg you're exempt from health, care and unemployment insurance contributions — only pension insurance applies. That leaves more net pay than a regular job.
Check your insuranceInternational students
Non-EU students can work 140 full or 280 half days per year (raised from 120/240 in March 2024). A working student contract usually fits within this — confirm the exact limits printed on your residence permit.
Studying in Germany
