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Vision System Data Science Engineer (all genders)

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Austria📅 13/10/2026

Descripción

1 Vision System Data Science Engineer (all genders) LOCATION: Villach, AT, 9500 REQ ID: 204410 What you'll do * Design, develop, and deploy computer vision algorithms for detection, classification, segmentation, anomaly detection, and predictive analytics. * Build scalable machine learning and deep learning models using image, video, and multimodal sensor data. * Lead the complete data science lifecycle, including data collection, labeling, feature engineering, model training, validation, deployment, and monitoring. * Develop robust image-processing pipelines utilizing modern computer vision frameworks and cloud technologies. * Collaborate with process engineers, software developers, hardware engineers, and product teams to deliver integrated vision solutions. * Conduct root-cause analysis and use advanced analytics to improve yield, process stability, and equipment performance. * Drive innovation by evaluating emerging AI, machine learning, and computer vision technologies. * Mentor junior engineers and provide technical guidance on best practices in data science and machine learning. * Translate analytical insights into standardized current best-known methods (cBKM) to enable scalable and sustainable business impact. * Design, develop, and maintain production-grade Python applications, libraries, and data science solutions following established software engineering best practices. * Process, structure, and analyze large-scale datasets while optimizing data pipelines, databases, and data architectures to support advanced analytics. Who we're looking for * Bachelor's or Master's degree in Data Science, Computer Science, Industrial Engineering, Applied Mathematics, or a related field. * Typically requires a minimum of 5 years of related experience with a Bachelor's degree; or 3 years and a Master's degree; or equivalent work experience. * Proven experience developing, deploying, and managing predictive maintenance or machine learning models in production environments
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