Data Scientist

🕒 January 19

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TRACTIAN

WebsiteLinkedInAll Job Openings

51 - 200 employees

🏢 Enterprise

⚡ Energy

Enterprise • Industrial • Energy

TRACTIAN is a company specializing in Asset Performance Management, providing industry-leading solutions to ensure zero downtime for manufacturing plants. With products like Smart Trac Condition Monitoring & Auto Diagnosis and TracOS™ Maintenance Management Software, TRACTIAN enables plant managers to predict failures, avoid downtime, and manage assets efficiently. Their solutions also focus on energy efficiency, transforming energy consumption data into profit. TRACTIAN collaborates with industries such as automotive, mining, oil and gas, and pharmaceuticals, offering advanced insights and AI-powered diagnostics to enhance machine reliability and operational efficiency.

📋 Description

• 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.