Data Scientist- RF/Acoustics Signal Processing

About this role

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data Scientist - RF/Acoustics Signal Processing based in the United States.
This role focuses on applying advanced data science and machine learning techniques to complex RF and acoustic signal environments. The successful candidate will transform high-volume, noisy time-series sensor data into actionable insights, diagnostics, and predictive models. Working fully remotely within the U.S. (excluding NY, CA, and IL), the role sits at the intersection of engineering, physics, and applied AI. You will collaborate closely with domain experts to solve ambiguous, real-world industrial and communications challenges. The position requires strong technical rigor in signal processing, along with the creativity to develop scalable ML solutions. It offers the opportunity to impact critical systems across industrial, telecommunications, and defense-adjacent applications. The environment is highly collaborative, data-driven, and innovation-focused.


Accountabilities

• Design and develop end-to-end signal processing and machine learning pipelines for RF, acoustic, and time-series sensor data, including techniques such as spectral analysis, beamforming, filtering, wavelet decomposition, and time-frequency analysis.
• Build, evaluate, and optimize ML models for anomaly detection, fault isolation, and predictive diagnostics across complex systems and noisy environments.
• Perform exploratory data analysis, feature engineering, and signal feature extraction on raw sensor data to identify patterns, anomalies, and system behavior.
• Support root cause analysis by interpreting RF and acoustic signals at subsystem, component, and system levels.
• Contribute to full ML lifecycle workflows, including data ingestion, model training, deployment, monitoring, and drift detection in production environments.
• Collaborate with engineering teams and subj...