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 #J-18808-Ljbffr Accounting Resources, Inc.