Data Science Intern

What you’ll do:

The Centre for Intelligent Power (CIP) is seeking interns to work within its multidisciplinary teams. If you are excited and inspired by the thought of contributing to a more sustainable global future, as well as bringing digitalization solutions and mindset to Eaton’s vast portfolio of products and services across multiple industries that our company operates.

In your role as a Data Science Intern, you will learn about the development and deployment of data science and analytics tools across Eaton’s product portfolio – from grid-scale insights to home energy management systems.

You will gain experience in building and releasing statistical, artificial intelligence (AI) and machine learning (ML) pipelines in a modern data science stack and will contribute to the ongoing development of Eaton’s Brightlayer Analytics platform. This platform provides a set of reusable analytics components which are used throughout the Eaton business to create products for Eaton’s customers. You will be involved in the design, development, and testing of edge and cloud-based systems.

You will also gain hands-on experience with the tools and approaches used as part of the design, development, and test processes.

What you can expect to learn:

At the end, we expect you to have gained a clear understanding of the end-to-end research, design, and development process for deploying intelligent solutions in Eaton products. You will have directly contributed to this design, experimental development, and test process for at least one proof of concept system. In addition, you will have the opportunity to present your work to the team and senior technical leadership within the company.

Qualifications:

Ongoing university studies (BSc/MSc) in Computer Science, Computer Engineering or similar

Skills:

• A basic technical background is required in addition to an interest and background in machine learning and statistical modelling.
• Familiarity with Python and/or R, and commonly used modelling/visualisation libraries (pandas, numpy, dplyr, ggplot2).
• Knowledge of foundational data science algorithms.
• Familiarity with embedded systems and embedded system programming.
• Knowledge of deep learning libraries such as TensorFlow, Keras, pytorch.
• Interest in reproducible research and software engineering best practices (coding best practices, version control).

Soft Skills:

• Analytical thinking
• Problem-solving
• Communication skills
• Team collaboration
• Time management
• Ownership