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Mid-level metro data role with broad skills and generalist title, high candidate density.
Requires ADAS, embedded and CAN expertise, limiting transferability across industries.
Explicit 6–8 years plus mandatory vehicle-CAN and embedded analytics raise filtering strictness.
Lead vehicle data mining and exploratory analytics using Python and data-science tools to identify anomalies and performance issues from CAN and Ethernet signals.
Support AI/ML workflows validating ADAS features including dataset exploration, labeling, and analysis of ML model behavior and performance metrics.
Perform embedded-level debugging through ECU log analysis and vehicle communication trace interpretation, and participate in vehicle testing and data collection for analytics validation.
6 to 8 years of relevant experience in data analytics and vehicle communications.
Bachelor’s or Master’s degree in Data Science, Computer Engineering, Electrical/Embedded Systems Engineering, Automotive, or Systems Engineering.
Proficiency in Python, Vehicle Communication protocols (CAN, Ethernet), data science analytics tools, MATLAB, Machine Learning Algorithms, DBQL/SQL, and Linux environment.
Hands-on experience with vehicle instrumentation and strong understanding of embedded systems fundamentals (ECUs, signals, logs).
Experienced in structured problem-solving in embedded automotive systems and adept at triaging vehicle sensor and software issues.
Demonstrates strong capability in collaborating with cross-functional teams including algorithm development, systems engineering, and validation under ADAS V-cycle processes.
Comfortable with hands-on vehicle testing, data instrumentation, and supporting continuous improvement of data analysis tools in automotive or ADAS domain.