This project pioneered Nielsen’s first machine learning system for classifying hardware faults in audience measurement devices. Built on minute-level time-series audio data and device metadata, the model replaced legacy threshold-based logic with an explainable, performant ML pipeline.
Deployed across more than 10 environments, the system reduced fault resolution time from 14 days to under 3 days, directly improving client reporting accuracy and operational efficiency.
- Modeled raw time-series signal data and metadata to detect hardware anomalies
- Designed, tested, and deployed a classification model that scaled across nationwide measurement devices
- Built explainable model diagnostics and integrated results into real-time analysis pipelines
- Received cross-functional recognition and an internal performance award
Tools & Stack
Python · scikit-learn · Pandas · NumPy · Time-Series Modeling · Classification · A/B Testing · Model Explainability
Demo and Access
Internal deployment at Nielsen. Technical summaries and architecture diagrams available upon request.