Data Scientist - Predictive Maintenance

Develop predictive maintenance algorithms using machine learning techniques for time-series data.

Analyze sensor data streams to identify patterns that predict equipment failure.

Research and stay up to date with academic literature and state-of-the-art condition monitoring techniques, translating relevant advances into practical solutions.

Collaborate with engineers to improve data pipelines and enhance model accuracy.

Build scalable, real-time models for low-latency predictions.

Create diagnostic tools that enable data-driven maintenance decisions.

Work with the laboratory team to design experiments and develop failure datasets using real machinery to validate hypotheses, develop new models, and optimize existing ones.

Continuously refine models based on real-world performance, experimental results, and feedback.

Expertise in machine learning, time-series analysis, and anomaly detection.

Proficiency in Python and common data science and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch).

Solid understanding of signal processing concepts and hands-on experience with industrial sensor data (e.g., vibration, current, temperature, pressure).

Ability to read, interpret, and apply insights from academic literature and state-of-the-art research in condition monitoring and fault diagnosis.

Experience designing experiments to validate hypotheses and benchmark models.

Strong problem-solving skills and ability to handle noisy, high-dimensional data.

Advanced English.

Familiarity with both academic research and real-world applications in condition monitoring, fault diagnosis, and prognostics (e.g., vibration-based methods, model-based vs. data-driven approaches).

Experience translating academic methods into robust, production-ready algorithms.

Prior experience working in industrial or manufacturing environments.