About this role

Your tasks

During your assignment you will delve deeply into the workings of industrial machines, analyzing failure modes and performance characteristics to develop a comprehensive understanding of their behavior.
Your area of responsibility includes cleaning, processing, and constructing meaningful features from multivariate sensor data sets to create an excellent basis for advanced modeling and analysis.
Take an active role in visualizing and analyzing complex data sets using time series charts, correlation matrices, and other exploratory techniques to uncover hidden relationships and interactions between key features.
Furthermore you will develop hybrid algorithms that combine domain expertise with state-of-the-art analytics, AI, and machine learning methods to generate actionable insights for predictive maintenance and system optimization from digital twin data.
From the first day you will accurately evaluate and validate models using labeled data sets and define performance baselines using relevant metrics such as accuracy, precision, and recall.
You work on creating scalable templates and integrating them into our digital twin platform to optimize deployment and ensure repeatability across different plants and customers.
Last but not least you will document methods, results, and technical workflows in a clear and structured manner to ensure knowledge transfer and the reproducibility of your solutions.

Education: studies in mechanical engineering, computational engineering, computer science, physics or comparable
Experience and Knowledge: strong skills in data analytics frameworks, machine learning algorithms (classification, regression, etc.), Git, and proficiency in Python programming is mandatory; experience in vibration monitoring (time and frequency domain analysis) or turbomachinery is preferred
Personality and Working Practice: you are a self-motivated, proactive and responsible person, with the ability to work effectively in cross-functional teams
Languages: business fluent in English

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