Mechanical Data Analyst – Python

ARi; Virginia; Presencial; USD 70 000 - 100 000; Tempo integral

Resumo da oferta

ARi seeks an entry-level Condition Monitoring Analytics Developer to develop data-driven solutions for predictive maintenance and equipment health monitoring. Focus areas include data science, machine learning, and signal processing for engines, pumps, and rotating machinery.

The candidate should have strong Python programming and analytical skills, with an understanding of mechanical systems and an interest in predictive models to foresee failures before they occur.

Qualificações

• Bachelor’s degree in Mechanical Engineering, Aerospace Engineering, Mechatronics, Computer Science, Data Science, or related quantitative field.
• 2+ years with Python (preferred) or R.
• Familiarity with 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 (engines, pumps, thermodynamics).
• Experience with data visualization tools such as Power BI, Tableau, Plotly, or Matplotlib.
• Strong analytical and problem-solving abilities.
• Ability to translate data insights into actionable recommendations.

Responsabilidades

• Develop, test, and validate predictive maintenance algorithms and ML models for industrial equipment.
• Design and implement anomaly detection to identify abnormal behavior and potential failures.
• Collect, clean, preprocess, and analyze high-frequency time-series data from IoT/SCADA/sensor telemetry.
• Perform feature engineering on vibration, temperature, pressure, and other sensor data.
• Apply FFT, spectral analysis, digital filtering, and time-domain analysis.
• Analyze failure patterns like bearing wear, cavitation, misalignment.
• Create Python-based analytics scripts using Pandas, NumPy, SciPy, Scikit-learn.
• Create dashboards and visualizations to communicate health trends and insights.
• Collaborate with Reliability, Mechanical, Data Science, and Software teams to validate models.
• Support deployment of analytics solutions into cloud or edge platforms.
• Troubleshoot data quality issues and improve model accuracy.
• Document model development, testing results, and findings.

Conhecimentos

Python

Pandas

NumPy

SciPy

Scikit-learn

Time-series analysis

FFT

Signal processing

Machine learning basics

Data visualization

Communication

Problem-solving

Formação académica

Mechanical Engineering

Aerospace Engineering

Data Science

Ferramentas

Power BI

Tableau

Plotly

Matplotlib

Descrição da oferta de emprego

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.

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