• $98,837 - $154,546, depending on years of experience

Role Overview:

• 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.

• 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.

• Successfully pass background check for cybersecurity site access.
• Strong foundation in signal processing theory and application, including experience with electrical, acoustic, or time-series data in a professional setting.
• Proficiency in Python for data manipulation, signal processing, and model development (NumPy, SciPy, pandas, scikit-learn, PyTorch or TensorFlow).
• Ability to work with uncertainty and incomplete information; comfortable forming and testing hypotheses when ground truth is limited.
• Clear communicator capable of translating technical signal processing and ML findings to non-specialist audiences.
• Self-directed and effective working remotely across cross-functional teams.
• Must reside in the United States; not accepting applicants in California, Illinois, or New York.

• Candidate will be responsible for reviewing policies and procedures related to cybersecurity and those relevant to the functions of their role.
• Candidate is expected to maintain a cybersecure work environment.

• 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