Data Scientist

You are a passionate AI professional, who is enthusiastic about automotive electronics and wants to define the future in-cabin user experience. Join our Automotive AI Lab as Data Scientist. Collaborating with global AI/ML, automotive and consumer audio expert teams, you will work on the next generation of innovative vehicle and IVI (in-vehicle infotainment) sound products. By applying analytical skills, statistical modeling, and machine learning algorithms to collect, analyze, and interpret large audio and related datasets, you will develop solutions for compelling sound experiences.

Minimum Qualification:

• Master's degree in computer science or comparable practical experience
• 6 years of industry experience with 4 years of experience in applied data science, and big data technologies
• Demonstrated experience across audio topics like microphone and speaker beamforming, noise suppression or cancellation, immersive audio, binaural audio, and strong DSP fundamentals for algorithm development and understanding of challenges for real-time deployment
• Strong understanding of state-of-the-art audio ML (Machine Learning) processing technologies and practical experience with AI/ML tech stacks, tool chains, data collection, synthetic data, simulators, and MLOps workflows (e.g. PyTorch, Kafka, Apache Spark, DataBricks, Python, R)
• experience in working with audio systems is a must - e.g experience in audio processing and products and knowledge in acoustics, psychoacoustics, acoustical benchmarking, sound quality, sound field measurement and equalization
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Preferred Qualification:

• Experience in embedded model deployment and using AI accelerators (e.g. GPU, NPU, DSP)
• Proficient coding skills in C++, MATLAB

Your responsibilites:

• Conceptualize and design AI/ML solutions for audio products 
• Mine and analyze large audio and related datasets
• Identify data correlations and select existing or develop custom models/algorithms 
• Assess the effectiveness and accuracy of data sources and data gathering techniques 
• Develop processes and tools to monitor and analyze model performance and data accuracy