Data Scientist - Signal Processing Engineer (Acoustics)

Cutsforth is a company focused on signal processing and data science applications. They are seeking a Data Scientist - Signal Processing Engineer to analyze electrical, vibration, and acoustic signals, transforming raw sensor data into actionable insights for industrial equipment maintenance.

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