Master Thesis Sales Time Series Foundation Models for Forecasting (f/m/x)

BMW Group; München; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A leading automotive company in Munich is seeking an intern to support the implementation of time series forecasting models. The successful candidate will work on projects related to large language models and machine learning. This internship offers comprehensive mentoring and flexible working hours, with a strong emphasis on developing practical skills. The ideal applicant is currently studying in a related field and has advanced knowledge of Python.

Leistungen

Comprehensive mentoring & onboarding

Personal & professional development

Flexible working hours

Mobile work

Attractive & fair compensation

Apartments for students

Qualifikationen

• Currently enrolled in related studies.
• Initial experience with machine learning fundamentals.
• Strong attention to detail and collaborative style.

Aufgaben

• Support implementation of time series foundation models.
• Build baselines and ensure reproducible experiments.
• Engineer temporal and exogenous features for forecasting.
• Document methods, code, results for internal reporting.

Kenntnisse

Interest in large language models

Time series methods knowledge

Problem-solving mindset

Fluent English

Ausbildung

Studies in computer science, mathematics, industrial engineering

Tools

Python

NumPy

SciPy

PyTorch

TensorFlow

Jobbeschreibung

Overview

THIS MUCH DOING ISN'T DOABLE AT JUST ANY COMPANY. SHARE YOUR PASSION. We believe in creating an environment where our interns really can learn by doing and where they are given their own areas of responsibility right from the start of their time with us. 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 is driving the digitalization of sales and supports the transition to fully data-driven decision-making and sales management. We encourage you to contribute your ideas and support you in gaining practical experience during your thesis.

What awaits you?

• You will support the implementation of time series foundation models (e.g., Chronos, Time-LLM, TimeGPT) for zero-shot forecasting, including probabilistic outputs.
• Furthermore, you help to build strong baselines (e.g., ARIMA, gradient boosted trees) and ensure fair, reproducible experiments.
• In addition, you will engineer temporal and exogenous features, handle seasonality, missing data and outliers.
• Moreover, you support the application of these methods to an automotive demand forecasting use case.
• You will also help to document methods, code and results, and contribute to a high-quality thesis and internal report.

Please note that your thesis must be supervised by a university on your part.

What should you bring along?

• Studies in computer science, mathematics, industrial engineering or a related technical field.
• Interest in and initial experience with large language models, time series methods and machine learning fundamentals.
• Advanced knowledge of Python programming and experience with numerical libraries such as NumPy, SciPy, PyTorch or TensorFlow.
• Strong problem-solving mindset, attention to detail and collaborative style in an interdisciplinary team.
• Fluent English skills.

Would you like to support our team in developing advanced time series forecasting models and gain practical experience? 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/whatweoffer

Start date: 03/02/2026

Duration: 6 months

Working hours: Full-time

Do you have questions? Then submit your inquiry easily via our contact form. Your inquiry will be answered by phone or email afterwards.

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.