Data Scientist at Cutsforth
Remote — full-time
Role Information: Job Title: Signal Processing Engineer (RF/Acoustics) Work Location: Fully remote position, home office- can NOT be located in NY, CA or IL 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. 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: Cutsforth.com/About Read our Mission & Values here: Cutsforth.com/Values Compensation: $98,837 - $154,546, depending on years of experience Role Overview: Applies data science and machine learning to the analysis of radio frequency and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights. Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions across industrial, communications, and defense-adjacent 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 RF, acoustic, and time-series sensor data, including beamforming, BSS, spectral subtraction, 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 demodulated RF and acoustic data to surface patterns and anomalies. Analyze and interpret signals from various electrical asset monitoring systems utilizing RF, acoustic, and signal processing expertise to support fault isolation and anomaly detection. Use asset monitoring sensor data as measurement to characterize and validate signal data. Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and LRU level — identifying root causes from spectral, RF, and acoustic sensor data in complex industrial systems. 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