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Data Scientist - Predictive Maintenance
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About the role
- •Data Science at TRACTIAN The Data Science team at TRACTIAN focuses on extracting valuable insights from vast amounts of industrial data.
- •Using advanced statistical methods, algorithms, and data visualization techniques, this team transforms raw data into actionable intelligence that drives decision-making across engineering, product development, and operational strategies.
- •The team constantly works on optimizing prediction models, identifying trends, and providing data-driven solutions that directly enhance the company’s operational efficiency and the quality of its products.
- •What you'll do As a Data Scientist
- •Predictive Maintenance at TRACTIAN, you will work at the intersection of advanced data science and industrial operations.
- •Your mission is to develop cutting-edge algorithms and predictive models to monitor and predict equipment failures before they occur, optimizing asset reliability and reducing downtime.
- •You’ll face complex challenges involving large-scale time-series data, real-time data processing, and machine learning applications, while collaborating closely with engineers and laboratory teams to ensure our predictive maintenance solutions remain industry-leading.
- •Responsibilities 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.
- •Requirements 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.
- •Bonus Points 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.