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About This Role
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:* $98,837 \- $175,000, depending on years of experience
Role Overview:
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Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time\-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment. Partners with engineering and domain experts to design and deploy production\-grade signal processing and ML solutions for predictive maintenance across industrial applications. Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking.
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.
Required Qualifications:* 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.
Preferred Qualifications:* 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.
Other Qualifications:* 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.
Cybersecurity Role Expectations:* 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.
Benefits:* 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
Alignment with Corporate Values
All Cutsforth employees are expected to perform their work in a manner that exhibits understanding and adherence to the Company Mission and Core Attributes of Cutsforth Employees. Employees in management roles must exhibit continual improvement along Cutsforth’s Leadership Traits. Further, each employee must read and adhere to corporate policies and safety protocols.
- Learn more about Cutsforth here, including our Mission \& Values: Cutsforth.com/About
Equal Employment Opportunity Statement:
Cutsforth will not discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, or national origin. Cutsforth will take affirmative action to ensure that applicants are employed, and that employees are treated during employment, without regard to their race, color, religion, sex, sexual orientation, gender identity, or national origin. Such action shall include, but not be limited to the following: Employment, upgrading, demotion, or transfer, recruitment or recruitment advertising; layoff or termination; rates of pay or other forms of compensation; and selection for training, including apprenticeship. Cutsforth agrees to post in conspicuous places, available to employees and applicants for employment, notices to be provided by the provisions of this nondiscrimination clause.
For Cutsforth's full Equal Employment Opportunity Policy, click here: EEO Notice to Employees \& Applicants
California Privacy Notice:
If you are a California resident, please review our California Job Applicant Privacy Policy for details regarding the personal information we collect during the hiring process, how we use it, and your rights under the CCPA. By submitting your application, you acknowledge that you have read and understand our privacy practices.
For Cutsforth's full CCPA Privacy Policy, click here CCPA: California Privacy Notice to Applicants
Washington State Fair Chance Act:
Cutsforth considers all qualified applicants, including those with criminal histories, in accordance with the Washington State Fair Chance Act. We do not automatically exclude applicants because of a criminal record. Any criminal background check occurs only after a conditional offer of employment, and any resulting decision is based on an individualized assessment of the record's relationship to the specific job.
Learn more about your rights and our process here: Fair Chance Act
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Salary Context
This $98K-$175K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Cutsforth, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($136K) sits 29% below the category median. Disclosed range: $98K to $175K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Cutsforth AI Hiring
Cutsforth has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $175K - $175K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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