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Mechanical Data Analyst – Python

ARi Virginia

Virginia

Today

$75k - $80k/yr Full time Python Analytics Anomaly Detection

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Job Description

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

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