使命
ob Summary
We are seeking an EE Quality Engineer (Systems Data Mining Specialist) to drive advanced field quality, reliability, and defect prevention for Electrical & Electronic (E/E) systems. In this role, you will leverage telematics data, vehicle diagnostic logs (DTCs), over-the-air (OTA) metrics, and warranty databases to mine, analyze, and translate complex vehicle system data into actionable quality improvements.
You will bridge the gap between Big Data analytics and hardware/software systems engineering, detecting emerging quality trends early and collaborating with cross-functional teams to resolve root-cause failures before they impact customer satisfaction.
Key Responsibilities
1. Data Mining & Field Quality Analytics
Build, execute, and maintain automated data pipelines and queries to extract quality insights from vehicle telematics, Diagnostic Trouble Codes (DTCs), warranty claims, OTA software logs, and customer feedback.
Apply statistical models, pattern recognition, and machine learning techniques to identify emerging E/E quality risks, software anomalies, and intermittent system behavior in production fleets.
Establish predictive quality indicators (e.g., early-warning algorithms) to flag potential field issues before traditional warranty reporting triggers occur.
2. EE Systems Root Cause & Technical Analysis
Partner with Systems Engineers, DREs, and Software Quality Engineers to correlate mined data with root-cause physical or software failure modes (e.g., CAN/LIN/Ethernet bus communication loss, voltage drops, ECU resets, sensors/actuators degradation).
Analyze raw vehicle bus logs (CAN trace files, PCAP) alongside cloud telemetry to reconstruct system-level failure scenarios.
Participate in root-cause investigations using structured frameworks (8D, 5 Whys, Ishikawa, Fishbone) to drive Permanent Corrective Actions (PCA).
3. Quality Metrics, Dashboards & Reporting
Develop, automate, and manage interactive quality dashboards (e.g., PowerBI, Tableau) to visualize E/E system health, warranty trends (R/1000, CPU), and recall risks for executive leadership and engineering teams.
Monitor software update (OTA) campaign success rates, post-flash error rates, and battery-drain telemetry.
Present data-backed technical findings during program quality reviews, gate approvals, and supplier quality syncs.
4. Supplier & Cross-Functional Quality Collaboration
Work with Tier-1 ECU/Software suppliers to reconcile field diagnostic data with component-level test results and manufacturing logs.
Provide data evidence to validate software bug fixes, EE design revisions, and hardware modifications.
Contribute to continuous improvement by updating DFMEA, PFMEA, and E/E quality standards based on insights gained from field data mining.
简介
Qualifications & Requirements
Education: Bachelor’s degree in Electrical Engineering, Computer Science, Data Science, Automotive Engineering, or a related technical discipline.
Experience: 3+ years of experience in E/E quality engineering, vehicle systems diagnostics, or automotive data analytics.
Data Mining & Analytics Skills:
Proficient in SQL for database queries and data extraction.
Strong programming capabilities in Python or R for data wrangling, statistical analysis, and script automation (using libraries like Pandas, NumPy, Scikit-Learn).
Experience with data visualization tools (Power BI, Tableau).
Automotive E/E Knowledge:
Solid understanding of automotive E/E architectures, ECUs, sensors, actuators, and communication protocols (CAN, LIN, Ethernet, FlexRay).
Familiarity with OBD-II diagnostics, UDS (ISO 14229), and Diagnostic Trouble Code (DTC) structures.
Experience interpreting vehicle log files using tools like Vector CANoe/CANalyzer, Wireshark, or similar.
Languages: Business fluency in English (written and spoken).
Preferred Qualifications
Experience working with OEM telematics platforms, cloud storage (AWS, Azure, GCP), or big data frameworks (Apache Spark, Databricks).
Knowledge of ASPICE, ISO 26222 (Functional Safety), or ISO 21434 (Cybersecurity).
Six Sigma Green/Black Belt certification.
Key Competencies
Analytical Curiosity: Driven to dig deep into unstructured data to uncover hidden system bugs and trends.
Translational Communication: Ability to articulate complex data insights into clear engineering and business terms for non-data science stakeholders.
System-Level Mindset: Capable of viewing failures holistically—connecting hardware, embedded software, and network interactions.