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Internship machine learning & cloud technologies for supply chain applications

Praktikum
Hotel du Parc
EUR 30’000 - EUR 80’000 pro Jahr
Inserat online seit: 9 April
Beschreibung

Internship Machine Learning & Cloud Technologies for Supply Chain Applications
Internship Objectives:

Application of AI & ML and advanced analytics on our Cloud foundation for optimizing supply chain processes

Developing the required data pipeline and foundation for a data backbone on our cloud infrastructure

Showing business benefit by achieving a sustainable improvement in SC KPIs

Responsibilities:

Carry out literature review and identify best practices in the use of ML, analytics and cloud technologies in inventory management, demand and capacity planning and SC optimization

Understand, extract, clean and structure the relevant business, SC and market big data

Learn & master the cloud apps and codes relevant to the project

Develop and integrate pipelines, algorithms and structure needed for the project

Benchmark and evaluate the results

Help integrating the new approaches into the processes and show the improvement of KPIs

Competencies acquired at the end of the Internship:

Hard skills: A strong foundational knowledge and experience in: Cloud technology, Supply chain analytics, ML for business planning and applications, Data pipelines, ML pipelines in cloud, Supply chain data and KPIs

Soft skills: Teamwork, Communication, Stakeholder management, Project management, Agile development, Translating business needs to technical solutions

Qualifications:

MSc or BSc in Engineering, Mathematics, Physics, Business Administration or similar

Good coding skills in Python

Knowledge and experience in machine learning (ML)

Familiarity with cloud platforms (preferentially Azure)

Strong analytical thinking, technical, communication and presentation skills

Job Details

Work location: 9469 Haag (Rheintal) (SG)

Workload: 100%

Employment start: 01.06.2026

Employment duration: 01.11.2026

Qualification: skilled

Work experience: Less than 1 year

Education: Master’s degree from a university or equivalent

English: Orally – Very good knowledge, Written – Very good knowledge

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