Elevate your assets to the next level
The Role
Develop and deploy signal-processing and ML solutions for electrical, vibration, and acoustic time-series from rotating equipment. Perform feature engineering, spectral and time-frequency analysis, fault isolation, end-to-end ML workflows, cross-sensor validation, and communicate results to technical and non-technical stakeholders while collaborating with engineering and domain experts.
Summary Generated by Built In
Role Information:
- Job Title: Data Scientist - Signal Processing Engineer- (Acoustics)
- Work Location: Fully remote position, home office
- Employment Type: Full-time
- Employment Status: Exempt, salaried
- Visa sponsorship is not available for this position.
- Must reside in the United States.
- We are not accepting applicants for remote workers in California, Illinois, and New York at this time.
Compensation:
Key Responsibilities:
- $98,837 - $175,000, depending on years of experience
Key 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.
- 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
Skills Required
- Bachelor's degree in Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Acoustical Engineering, Mechanical Engineering, Aerospace Engineering, or closely related discipline
- 5+ years professional experience in data science, machine learning, or applied signal processing with electrical/current or industrial sensor signal data
- Direct industry experience in 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, feature extraction, wavelets, matched filtering, and time-frequency analysis
- Proficiency in Python and scientific/ML libraries (NumPy, SciPy, pandas, scikit-learn, PyTorch or TensorFlow)
- Familiarity with electrical measurement and analysis workflows (current/voltage waveform capture, power quality analyzers, instrumentation)
- Strong analytical and problem-solving skills and ability to work in ambiguous or data-sparse problem spaces
- Excellent written and verbal communication; ability to present technical findings to non-technical audiences
- Must reside in the United States; applicants from California, Illinois, and New York are not being accepted
- Visa sponsorship is not available
- Successfully pass background check for cybersecurity site access
- Self-directed and effective working remotely across cross-functional teams
- Master's degree in related field
- Experience with Electrical Signature Analysis (ESA) or Motor Current Signature Analysis (MCSA)
- Familiarity with rotating machinery fault physics (bearing faults, eccentricity, winding faults, broken rotor bars)
- Demonstrated ability to take ML models from prototype to 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)
- Experience with physics-informed modeling approaches
- Active participation in signal processing or data science community through publications, open-source, or conferences
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The Company
What We Do
Cutsforth™ specializes in developing innovative new technologies and services to enhance plant asset management. We create mechanical, electrical, and software-based solutions to help plants monitor and maintain their critical equipment. Gain real-time insight into your machinery health through asset monitoring solutions and machinery upgrades. Cutsforth's knowledge and commitment to excellence drives our innovative solutions for the changing needs of our customers. Whether a quick response to a critical situation, or a new way of solving an old problem, our commitment to quality ensures our customers receive the best in class products and services---Cutsforth is the Power of Innovation.
