Mechanical Data Analyst – Python

This entry-level Condition Monitoring Analytics Developer role applies data science and signal processing to predictive maintenance and equipment health monitoring.

Responsibilities

• Develop, test, and validate predictive maintenance algorithms and machine learning models for industrial equipment, including engines and pumps
• Design and implement anomaly detection methods to flag abnormal 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 information from vibration, temperature, pressure, and other sensor signals
• Use signal processing techniques including Fast Fourier Transform (FFT), spectral analysis, digital filtering, and time-domain analysis
• Study mechanical failure patterns such as bearing wear, cavitation, misalignment, and other equipment performance issues
• Build 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
• Partner with Reliability Engineers, Mechanical Engineers, Data Scientists, and Software Development teams to validate analytical models
• Support integration and deployment of predictive analytics solutions into cloud-based or edge-computing environments
• Troubleshoot data quality issues, improve model accuracy, and support continuous improvement of condition monitoring solutions
• Document model development, testing results, and technical findings

Requirements

• 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: Pandas, NumPy, SciPy, Scikit-learn
• Basic understanding of machine learning, predictive modeling, and anomaly detection
• Knowledge of time-series 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: Power BI, Tableau, Plotly, or Matplotlib
• Strong analytical, troubleshooting, and problem-solving skills
• Ability to communicate technical findings and translate insights into actionable recommendations

Tools & Technologies

• Python, R
• Pandas, NumPy, SciPy, Scikit-learn
• Fast Fourier Transform (FFT)
• Power BI, Tableau, Plotly, Matplotlib
• IoT sensors, SCADA systems

Preferred Skills

• Academic projects, internships, or hands-on experience in 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 related industrial equipment
• Exposure to IoT platforms, SCADA systems, or fleet telemetry data
• Experience with machine learning 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

Location: Virginia (onsite) (Virginia Beach, Virginia)

Salary: USD 75,000 - 80,000 per year

Reference: JOB-1454