Calibration Process Data Science Intern (8 months/40 hours per week)

Bosch Group•Farmington Hills, MI

7d•Onsite

About The Position

The internship will be from either January - August 2027, or May - December 2027. This internship takes place in Farmington Hills, Michigan, the headquarters of Bosch North America, in the Calibration Engineering group of Power Solutions. The successful candidate will be given an 8-month project, that has been defined in advance by their Engineering Mentor. As a Data Science Intern at Bosch, you will have the opportunity to work on innovative projects and gain hands-on experience in the field of data science focused on automotive applications. You will be part of a dynamic team, supporting experienced data scientists and engineers to solve real-world automotive powertrain problems using advanced analytics and machine learning techniques. If you're interested to work in a collaborative, learning culture where interns work on meaningful projects, join our team!

Requirements

Must be currently enrolled in an accredited university and pursuing a Bachelors or Masters degree in Engineering, with at least one semester completed
Very good Python skills and has written own scripts (not just auto code)
Has experience with Artificial Intelligence and Machine Learning algorithms (work or academic)
1+ year academic or industry experience with software languages and operating systems.
A minimum 3.0 GPA
Must be a minimum of 18 years of age
Must work 35-40 hours per week

Nice To Haves

Automotive-related hobby and/or experience working with engines or vehicles.
Experience with LLM (large language model), NN (neural networks)
Projects in automotive field
Other programming languages
Experience with PowerBI
Leadership / Ownership experience

Responsibilities

Collect, clean, and analyze large and complex datasets to identify patterns, trends, and insights. Apply statistical methods and data visualization techniques to communicate findings effectively.
Develop and implement machine learning models, algorithms, and statistical techniques to solve specific business challenges. This may involve tasks such as data preprocessing, feature engineering, model training, and evaluation.
Build predictive models to forecast future outcomes and optimize business processes. Apply regression, classification, clustering, and other machine learning techniques to solve problems related to demand forecasting, anomaly detection, quality improvement, etc.
Discover meaningful patterns, correlations, and relationships in large datasets. Use techniques like association rules, clustering, and dimensionality reduction to uncover insights and generate actionable recommendations.
Work closely with cross-functional teams, including business stakeholders and software developers, to understand requirements and develop data-driven solutions. Collaborate effectively, share knowledge, and contribute to the overall success of the projects.
Document methodologies, algorithms, and results in a clear and concise manner. Prepare reports and presentations to communicate findings and recommendations to both technical and non-technical audiences.
Stay up-to-date with the latest advancements in data science, machine learning, and related technologies. Actively participate in training programs, workshops, and knowledge-sharing activities to enhance your skills and expertise.