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IBM

Studentenpraktikum Data Science (f/m/x)

IBM

Published 14 May 2026
Austria
Intern

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Role Highlights

Languages used

Java
Python
SQL

Key skills

Machine Learning
CICD
Clean Code
Cloud
AI
Data
Scrum
Data Science

Tools, Libraries and Frameworks

IOS
Red Hat
IBM
PyTorch
Tensorflow

Description

The role involves working within a project team to address complex problems and develop new approaches. The individual will contribute to machine learning initiatives while collaborating in a scrum environment. The position emphasizes the application of clean code principles and the utilization of various design patterns. Team members engage in constant knowledge sharing and professional development. The work focuses on delivering innovative solutions through effective communication and collaborative efforts.

Required Qualifications and Skills

Candidates must be currently enrolled in a technical or computer science degree program at a university or technical college for the duration of the internship. Proficiency in Java or Python is required, alongside knowledge of SQL databases and clean code practices. Applicants must possess excellent German language skills and good proficiency in English. Familiarity with DevOps and CI/CD processes is considered an additional asset.

Disclaimer

Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies. The full external job listing link should always be relied on for authoritative information.

About the company

IBM

Size

305978

Website

ibm.com

HQ

Armonk, New York, US

Public/Private

Public Company

Description

IBM infuses core business operations with intelligence, from machine learning to generative AI, to make organizations more responsive, productive, and resilient. It helps clients put AI into action now, creating real value with trust, speed, and confidence across various areas like digital labor, IT automation, and security. The ability to utilize all data is critical, as AI's effectiveness is dependent on the quality of data fueling it, with IBM's AI, and data platform aiming to scale and accelerate AI's impact with trusted data. IBM's hybrid cloud platform offers a comprehensive approach to development, security, and operations across hybrid environments, laying a flexible foundation for leveraging data wherever it resides.

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