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 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.
• Collaborate with engineers to improve data pipelines and enhance model accuracy.
• Build scalable, real-time models for low-latency predictions.
• Create diagnostic tools for technicians to make data-driven maintenance decisions.
• Continuously refine models based on real-world performance and feedback.

Requirements

• Experience in predictive maintenance and condition monitoring.
• Expertise in machine learning, time-series analysis, and anomaly detection.
• Proficiency in Python
• Knowledge of signal processing and industrial sensor data.
• Experience with real-time data pipelines and cloud platforms.
• Strong problem-solving skills and ability to handle noisy, high-dimensional data.