Data Scientist – RF/Acoustics Signal Processing

• 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.
• 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 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, 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 RF, acoustic, ultrasonic, or communications signal data.
• Direct industry experience in one or more of: Aerospace, Telecommunications, Military/Defense communications, Industrial Acoustics, or RF/Electronic Systems.
• Hands‑on experience with time‑series and signal processing techniques, including spectral analysis, filtering, and feature extraction from raw sensor or radio data.
• Proficiency in Python, including scientific computing libraries (Num Py, Sci Py, pandas) and ML frameworks (scikit‑learn, Py Torch, or Tensor Flow).
• Demonstrated use of RF measurement and analysis workflows, including use of spectrum analyzers, network analyzers, signal generators, and oscilloscopes in a professional engineering context.
• 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.
• Knowledge of Electromagnetic Compliance techniques.
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