R&D Machine learning

As a family-run, high-tech company with nearly 19,000 employees at 71 locations worldwide, we are looking for forward thinkers with unconventional ideas and drive to join our team. Our company culture, which values collaboration and mutual trust, creates the ideal framework for boldly trying new things and questioning the status quo. Our technologies inspire people to develop and produce things that are currently unimaginable. Whether lasers, machine tools, EUV or electronics - TRUMPF is building technological worlds for future generations. Are you ready for new challenges?

The SCC Machine Analytics team transforms large-scale machine status, sensor, and process data into practical tools that help TRUMPF technical service and engineering teams understand machine conditions, diagnose issues, and make faster, more confident decisions. As a R&D Machine Analysist, you will combine software engineering, data analytics, statistical modeling, and machine-learning expertise to develop reliable data-driven products for machine monitoring and technical support. You will build and maintain internal web applications, dashboards, APIs, and data pipelines while developing and validating algorithms for condition assessment, anomaly detection, issue identification, failure diagnosis, and forecasting. This role requires close collaboration with technical service, machine R&D, testing, and other business partners to translate real-world diagnostic challenges into dependable analytical solutions.

Duties & Responsibilities

• Develop and maintain internal web applications, dashboards, APIs, data pipelines, and analytics tools supporting technical service and engineering operations.
• Analyze machine, sensor, process, and operational data to diagnose complex issues, identify patterns and root causes, and support machine monitoring.
• Develop, validate, and maintain statistical, predictive, and machine-learning models for condition assessment, anomaly detection, failure diagnosis and prediction, time-series analysis, and forecasting.
• Apply advanced statistical, mathematical, and analytical methods to derive actionable insights from structured and unstructured data.

• Design and implement efficient Python solutions while working with relational databases and T-SQL to deliver robust ETL pipelines, seamless data integration, optimized query performance, and high-quality data management.

• Apply software-engineering and DevOps practices for testing, version control, deployment, monitoring, containerization, and maintenance.
• Collaborate with technical service, R&D, testing, product specialists, and other stakeholders to translate diagnostic challenges into practical data-driven solutions.
• Integrate analytical results into user-friendly tools that improve troubleshooting and decision-making.

• Translate complex analytical findings and model performance metrics into clear, actionable recommendations for technical and non-technical stakeholders.