Working Student Medical ML Data Management (f/m/x)

München, Germany

ZEISS Group

ZEISS is an international leading technology enterprise operating in the optics and optoelectronics industries. 

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Seeing beyond - future of medical technology

For more than 100 years, ZEISS medical technology has been driving progress in ophthalmology and microsurgery. In ophthalmology, our solutions help to maintain and improve people's vision at every stage of their lives. In microsurgery, our solutions enable targeted interventions on diseased tissue so that small and sensitive organs continue to function optimally.

Medical technology from ZEISS supports medical professionals in improving the lives of patients worldwide. The fact that our actions have a direct impact on people's well-being is what drives us every day.

Your role
  • Preparing data for machine learning tasks

  • Designing and developing the quality criteria for high quality data generation

  • Understanding the variety of data and extracting valuable information out of it

  • Generating the knowledge graph and queries to make data products for AI development

Your profile
  • Student in master’s degree in machine learning, computer science, informatics, or a related engineering field

  • Theoretical knowledge of statistical analysis, machine learning, data mining and data engineering

  • Proficiency at Python and C++ programming

  • Experience with database management systems and similar data management tools

  • Having experience or knowledge developing SPARQL queries is plus

  • Fluency in English

  • Ability to collaborate productively with the team

  • Nice to have basic knowledge of cloud infrastructure

  • Nice to have basic knowledge of Kubernetes and docker-based environments

Your ZEISS Recruiting Team:

Laura Steininger
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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

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Tags: Computer Science Data management Data Mining Docker Engineering Kubernetes Machine Learning Python Statistics

Region: Europe
Country: Germany

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