Key skills
Signal ProcessingMachine LearningElectrical Signal AnalysisVibration AnalysisAcoustic Signal AnalysisTime-Series Data AnalysisSymmetrical Component AnalysisMatched FilteringWavelet DecompositionTime-Frequency AnalysisFeature EngineeringPythonNumPySciPypandasscikit-learnPyTorchTensorFlowElectrical Signature AnalysisMotor Current Signature AnalysisCloud Platforms AWSCloud Platforms AzureCloud Platforms GCPMLOps Tooling MLflowMLOps Tooling DockerMLOps Tooling AirflowMLOps Tooling CI/CD pipelinesAIMLPandasMLOpsMLflowAirflowAWSAzureGCPDockerCI/CDCommunication
About this role
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