Cutsforth is focused on applying data science and machine learning to analyze electrical, vibration, and acoustic signals. The Data Scientist - Signal Processing Engineer will transform raw sensor data into actionable insights and collaborate with engineering experts to design and deploy signal processing solutions for predictive maintenance in industrial applications.

Responsibilities:

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:

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
Master's degree in Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Data Science, or a related field
Experience with Electrical Signature Analysis (ESA), Motor Current Signature Analysis (MCSA), or similar electrical machine diagnostic techniques
Familiarity with rotating machinery fault physics (bearing fault frequencies, eccentricity, winding faults, broken rotor bars)
Demonstrated ability to own an ML model from prototype through production, including monitoring and retraining
Familiarity with array/multi-sensor signal fusion across electrical and vibration domains
Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps tooling (MLflow, Docker, Airflow, CI/CD pipelines)
Experience with physics-informed modeling approaches
Active participation in the broader signal processing or data science community through publications, open-source projects, or conference presentations

Data Scientist Contractor

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