Mechanical Data Analyst – Python
The Mechanical Data Analyst role at Confidential Client involves developing and implementing data-driven predictive maintenance solutions for industrial equipment such as engines and pumps. The position requires strong Python programming and analytical skills to analyze sensor data, apply machine learning and signal processing techniques, and collaborate with engineering teams to improve equipment health monitoring and failure prediction.
About Careertakes
👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Engineering Services.
Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.
Overview (Confidential Client — Virginia Beach, VA)
Confidential client, a firm in Engineering Services, is hiring an entry-level Mechanical Data Analyst (Python) to help develop predictive maintenance and condition-monitoring solutions for industrial equipment (engines, pumps, rotating machinery). This role blends mechanical engineering knowledge, signal processing, and Python-based data science to detect anomalies and predict failures from high-frequency sensor data.
What You’ll Do
- Develop, test, and validate predictive maintenance algorithms and machine learning models for engines, pumps, and rotating machinery.
- Design and implement anomaly-detection techniques to surface abnormal equipment behavior.
- Collect, clean, and preprocess high-frequency time-series data from IoT sensors, SCADA systems, and telemetry.
- Perform feature engineering on vibration, temperature, pressure, and other sensor streams.
- Apply signal-processing techniques (FFT, spectral analysis, digital filtering, time-domain analysis).
- Build Python scripts and models using Pandas, NumPy, SciPy, and Scikit-learn.
- Create dashboards and visualizations (Power BI, Tableau, Plotly, Matplotlib) to communicate equipment health and predictive insights.
- Collaborate with Reliability and Mechanical Engineers, Data Scientists, and Software teams to validate and deploy models to cloud or edge platforms.
- Troubleshoot data-quality issues and iterate on model accuracy.
- Document model development, testing, and technical findings for operational handoff.
Qualifications (Required)
- Bachelor’s degree in Mechanical Engineering, Aerospace Engineering, Mechatronics, Computer Science, Data Science, or a related quantitative field.
- 2+ years of experience with strong Python programming skills (preferred).
- Hands-on 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 (FFT, filtering).
- Solid grounding in mechanical engineering fundamentals (engines, pumps, failure modes).
- Experience with visualization tools (Power BI, Tableau, Plotly, Matplotlib).
- Strong analytical, troubleshooting, and communication skills — able to translate technical results into actionable recommendations.
Preferred Skills
- Academic projects, internships, or work in predictive maintenance, condition monitoring, or equipment-health analytics.
- Experience analyzing vibration, temperature, or pressure sensor data and working with high-frequency time-series.
- Familiarity with rotating machinery (bearings, misalignment, cavitation), internal combustion engines, or centrifugal pumps.
- Exposure to IoT platforms, SCADA systems, telemetry ingestion, cloud analytics, or edge deployments.
- Experience applying ML algorithms for anomaly detection, classification, regression, or failure prediction.
- Knowledge of reliability engineering and condition-based maintenance practices.
Compensation & Location
- Location: Virginia Beach, VA (on-site or as required by the confidential client)
- Salary range: $75,000 - $80,000 per year (listed per source). Actual compensation may vary based on experience and location.
Why Join (What We Offer)
- Hands-on experience building models that directly impact asset reliability and maintenance decisions.
- Cross-disciplinary collaboration with engineers and data teams in a fast-growing Engineering Services environment.
- Professional growth opportunities in condition monitoring, reliability engineering, and applied ML.
- Access to real-world industrial datasets and modern deployment platforms (cloud and edge).
Equal Opportunity & Hiring Transparency
Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.
If you have questions about how your data is used, please contact us directly.
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