Mechanical Data Analyst - Python

Job Description

ARi is a fast-growing global engineering firm headquartered in East Peoria, IL, USA. Our power of "I" represents the innovation, intelligence, ingenuity, and insight that differentiates us from our competition. With 1,500+ engineering talent worldwide, representing multiple nationalities and countries, we are well-positioned to deliver sophisticated and custom solutions to our clients

Job Summary

We are seeking an entry-level Condition Monitoring Analytics Developer to develop, test, and implement data-driven solutions for predictive maintenance and equipment health monitoring. This role focuses on applying data science, machine learning, and signal processing techniques to analyze the performance of industrial equipment, particularly engines, pumps, and rotating machinery.

The ideal candidate will have strong Python programming and analytical skills, an understanding of mechanical systems, and an interest in developing predictive models that identify potential equipment failures before they occur.

Duties & Responsibilities

• Develop, test, and validate predictive maintenance algorithms and machine learning models for industrial equipment, including engines and pumps.
• Design and implement anomaly detection techniques to identify abnormal equipment behavior and potential mechanical failures.
• Collect, clean, preprocess, and analyze high-frequency time-series data from IoT sensors, SCADA systems, and equipment telemetry.
• Perform feature engineering and extract meaningful information from vibration, temperature, pressure, and other sensor data.
• Apply signal processing techniques such as Fast Fourier Transform (FFT), spectral analysis, digital filtering, and time-domain analysis.
• Analyze mechanical failure patterns, including bearing wear, cavitation, misalignment, and other equipment performance issues.
• Develop Python-based scripts and analytical models using Pandas, NumPy, SciPy, and Scikit-learn.
• Create dashboards, reports, and visualizations to communicate equipment health trends and predictive maintenance insights.
• Collaborate with Reliability Engineers, Mechanical Engineers, Data Scientists, and Software Development teams to validate analytical models.
• Support the integration and deployment of predictive analytics solutions into cloud-based or edge-computing platforms.
• Troubleshoot data quality issues, improve model accuracy, and support continuous improvement of condition monitoring solutions.
• Document model development, testing results, and technical findings.

Requires Skills

• Bachelor's degree in Mechanical Engineering, Aerospace Engineering, Mechatronics, Computer Science, Data Science, or a related quantitative field.
• 2+ years with strong programming skills in Python (preferred) or R.
• Familiarity with Python libraries such as Pandas, NumPy, SciPy, and Scikit-learn.
• Basic understanding of machine learning, predictive modeling, and anomaly detection.
• Knowledge of time-series data analysis and signal processing concepts, including FFT and digital filtering.
• Understanding of mechanical engineering fundamentals, including engines, pumps, thermodynamics, fluid mechanics, and mechanical failure modes.
• Experience with data visualization tools such as Power BI, Tableau, Plotly, or Matplotlib.
• Strong analytical, troubleshooting, and problem-solving abilities.
• Ability to communicate technical findings and translate data insights into actionable recommendations.

Preferred Skills

• Academic projects, internships, or hands-on experience involving predictive maintenance, condition monitoring, or equipment health analytics.
• Experience analyzing vibration, temperature, pressure, or other industrial sensor data.
• Familiarity with rotating machinery, internal combustion engines, centrifugal pumps, or industrial equipment.
• Exposure to IoT platforms, SCADA systems, or fleet telemetry data.
• Experience with machine learning algorithms for anomaly detection, classification, regression, or failure prediction.
• Familiarity with cloud-based analytics platforms or edge-computing applications.
• Knowledge of reliability engineering, failure analysis, or condition-based maintenance.
• Experience working with high-frequency time-series data.