Ford Motor Company

Developed an ML proof-of-concept (TensorFlow + Scikit-learn) trained on 50K+ connectivity samples to classify network anomalies, outperforming baseline rule-based detection. Created Jenkins pipelines (Docker + MQTT) for automated infotainment fault validation, running 100+ tests per nightly build and eliminating manual QA loops. Built a Linux-based Slash test suite covering 250+ regression cases across multiple firmware releases, improving reliability in pre-production environments. Analyzed IPv6 connectivity logs via Pandas/NumPy to identify gaps and improve signal accuracy by 32%.