Machine Learning Engineer at ARTIDIS AG (Basel, Switzerland)

ARTIDIS is running large clinical studies which generate high quantities of numerical, text, visual, and user-generated data. It is the responsibility of the Machine Learning (ML) engineer to find innovative ways to use those data to find new modalities (bio-markers) for cancer diagnostic.

This will involve researching techniques in statistics and ML literature for novel approaches to the problem at hand, developing a prototype to assess the viability of the approach, and implementing the solution as an application that can be deployed into a production environment.

Additionally, ML engineers will have close relationships with the product development team and will serve as the technical aid in generating ideas for new ML features in the ARTIDIS data analysis software. As such, it is critical for ML engineers to be able to communicate to non-experts complex ML concepts and any specific data requirements for prospective features.

Responsibilites

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• Research, design, and implement machine learning applications to solve business challenges in diagnostic biomarker filed
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• Keep up-to-date with new technologies that may be able to support machine learning applications.
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• Aid product development teams with new ideas for machine learning applications
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• Understand necessary data requirements to implement machine learning applications
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• Able to prototype machine learning applications and quickly determine application viability
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Skills and Qualifications

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• Strong quantitative background:
  
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  • A master’s degree in Computer Science, Computational Linguistics, Physics, Mathematics or related field.
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  • A PhD is preferred.
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• A background in independent research.
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• Some experience in experimental design.
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• Experience building machine learning applications using numerical and/or text data.
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• Solid understanding of machine learning and statistics fundamentals.
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• Ability to transform “raw” data to conform to the assumptions of a machine learning
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Algorithm

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• Strong proficiency in Python as well as the numerical libraries such as Scikit-Learn, Pandas, NumPy, and SciPy.
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• Experience with some or all of the following:
  
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  • REST APIs
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  • SQL
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  • Amazon Web Services
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• Ability to effectively communicate complex ideas to other members of the team.
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