Thesis Causal ML for Prescriptive Decision Support in Automotive Goodwill (f/m/x)

BMW AG · Munich

ThesisGerman needed: fluent

Still open on 2 Nov

You apply on the company's own website.

Key facts

Pay: Good and fair pay
Duration: 6 months
Where you work: Company and home
Language needed: German and English
Subject you study: Computer Science, Business Informatics, Data Science, Statistics, Mathematics, Business Mathematics

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.

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

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.

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