Data Scientist – Predictive Maintenance

Cutsforth Inc.

Data Scientist applies data science and machine learning for predictive maintenance at Cutsforth LLC. Transforming raw sensor data into actionable diagnostics for industrial equipment.

Posted 7/17/2026full-timeRemote • California, Illinois, New York • 🇺🇸 United StatesMid-LevelSenior💰 $98,837 - $154,546 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data science and machine learning applied to signal processing for industrial equipment, with a strong focus on time-series analysis and fault detection. Proficient in Python and relevant scientific libraries, capable of translating complex data challenges into actionable insights for diverse stakeholders.

Highest-signal resume keywords

Data ScienceMachine LearningSignal ProcessingPython ProgrammingTime-Series Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisFeature EngineeringSignal Feature ExtractionSpectral AnalysisFiltering TechniquesElectrical Signature AnalysisVibration AnalysisPredictive MaintenanceModel TrainingAlgorithm Evaluation

Soft Skills

Analytical SkillsProblem-SolvingCommunication Skills

Tools & Technologies

NumPySciPyPandasScikit-learnPyTorchTensorFlow

Industry Keywords

Industrial EquipmentCondition MonitoringElectrical Machine DiagnosticsPower SystemsAcoustic Signals

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment.
Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions for predictive maintenance across industrial applications.
Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking.
Design and develop signal processing pipelines and machine learning models that operate on electrical (current/voltage), vibration, and acoustic time-series sensor data, including symmetrical component analysis, matched filtering, wavelet decomposition, and time-frequency analysis techniques.
Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable.
Perform exploratory data analysis, feature engineering, and signal feature extraction on raw electrical, vibration, and acoustic data to surface fault patterns and anomalies.
Analyze and interpret signals from electrical asset monitoring systems (motors, generators, pumps) utilizing electrical signature analysis, vibration analysis, and signal processing expertise to support fault isolation and anomaly detection.
Use cross-sensor asset monitoring data (temperature, speed, load) to characterize and validate signal-derived diagnostics.
Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and machine level, identifying root causes from spectral, electrical, and vibration sensor data in rotating industrial equipment.
Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments.
Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions.
Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders.
Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.

Requirements

What you’ll need

Bachelor’s degree in Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Acoustical Engineering, Mechanical Engineering, Aerospace Engineering, or a closely related engineering discipline required.
5+ years of professional experience in data science, machine learning, or applied signal processing, with demonstrated work on electrical, current/voltage, or industrial sensor signal data.
Direct industry experience in one or more of: Industrial/Rotating Equipment, Power Systems, Electrical Machine Diagnostics, or Condition Monitoring.
Hands-on experience with time-series and signal processing techniques, including spectral analysis, filtering, and feature extraction from raw sensor data.
Proficiency in Python, including scientific computing libraries (NumPy, SciPy, pandas) and ML frameworks (scikit-learn, PyTorch, or TensorFlow).
Familiarity with electrical measurement and analysis workflows (e.g., current/voltage waveform capture, power quality analyzers, or equivalent instrumentation).
Strong analytical and problem-solving skills with the capacity to work through ambiguous or data-sparse problem spaces.
Excellent written and verbal communication skills; ability to present technical findings to non-technical audiences.

Benefits

Comp & perks

Paid Time Off
Medical, Vision, Dental Insurance
Health Savings Account with Employer contributions
401(k) with Employer match
Short-term & Long-term Disability Coverage
Accidental Death & Dismemberment Coverage
Life Insurance Coverage
Eight paid holidays per year
All other benefits required by applicable law