Mechanical Data Analyst – Python

Accounting Resources, Inc.; Virginia; On-site; USD 75,000 - 80,000; Full time

Job summary

Accounting Resources, Inc. is seeking an entry-level Condition Monitoring Analytics Developer to apply data science and signal processing to predictive maintenance and equipment health monitoring.

The role involves developing algorithms for engines and pumps, anomaly detection, and working with IoT/SCADA data. The candidate will build Python scripts and analytical models using Pandas, NumPy, SciPy, Scikit-learn, and create dashboards to visualize health trends.

Qualifications

• Bachelor’s degree in a quantitative field (Engineering or Data Science) is required.
• 2+ years of programming with Python or R.
• Familiarity with Python data libraries and ML basics.
• Knowledge of time-series analysis and signal processing concepts.
• Experience with data visualization tools (Power BI/Tableau/Plotly/Matplotlib).

Responsibilities

• Develop, test, and validate predictive maintenance algorithms and ML models for industrial equipment.
• Design anomaly detection methods to flag failures.
• Collect, clean, preprocess and analyze high-frequency time-series data from IoT sensors, SCADA systems, and telemetry.
• Perform feature engineering on signals like vibration, temperature, and pressure.
• Apply FFT, spectral analysis, filtering, and time-domain methods.
• Create dashboards and reports to communicate health trends and insights.
• Collaborate with engineers, data scientists, and software teams to validate models.
• Support deployment of analytics into cloud or edge environments.
• Troubleshoot data quality issues and improve model accuracy.
• Document development, testing results, and findings.

Skills

Analytical thinking

Problem solving

Communication of findings

Time-series analysis knowledge

Education

Bachelor’s degree in Mechanical Engineering, Aerospace Engineering, Mechatronics, Computer Science, Data Science, or related field

Tools

Python

R

Pandas

NumPy

SciPy

Scikit-learn

Power BI

Tableau

Plotly

Matplotlib

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