Working Student - Data Science

at  Audatic GmbH

AUDATIC GMBH - R&D

At Audatic, we build systems to render speech from any sound scenario clear and free of noise using state-of-the-art deep learning technology, enabling millions of people with hearing loss to enjoy social interactions in noisy environments like bars or restaurants.
Working Student - Data Science

WORKING FIELD:

Kick start your data science career with us as a Working Student! We are looking for a motivated data science or computer science major who can work for 15-20 hours per week for at least 12 months. At Audatic, you will closely collaborate with our team of Data Scientists, Deep Learning Engineers, and Software Engineers, supporting them in daily tasks.

Responsibilities include:

• Create, test, and improve datasets for a variety of audio environments and settings
• Collaborate on the design and development of audio quality metrics
• Support the development and testing of audio pre- and post-processing methods
• Analyze how datasets, preprocessing methods and other training components affect the performance of neural networks, while assessing the quality of the metrics used
• Help maintain our software infrastructure

REQUIREMENTS:

• Enrolled in a Data Science, Computer Science, or similar quantitative degree program - please note that to be eligible for the student position you must be enrolled at a university for the entire duration of employment
• Solid knowledge and understanding of statistical and Data Science techniques (such as regression analysis, machine learning algorithms, data visualization, etc.)
• Strong Python skills and knowledge of the Python scientific stack (NumPy, SciPy, Pandas, Matplotlib, etc.)
• Ability and willingness to quickly learn new tools and techniques
• Fluent English and excellent communication skills
• Passion for deep learning and medical technology

• Create, test, and improve datasets for a variety of audio environments and settings
• Collaborate on the design and development of audio quality metrics
• Support the development and testing of audio pre- and post-processing methods
• Analyze how datasets, preprocessing methods and other training components affect the performance of neural networks, while assessing the quality of the metrics used
• Help maintain our software infrastructur