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1. Position Purpose
Analyze operational data from the test fleet (CAN/Ethernet/LIN logs, sensor data, video) to evaluate ADAS/ADS system performance, identify issues, and provide data evidence supporting vehicle quality and safety related to ADS systems.
Build reports, dashboards, and KPIs to provide clear and visual insights for system performance evaluation.
2. Key Responsibilities
- Log Data Collection & Processing: Extract and decode CAN/CAN-FD log data (based on DBC files, Automotive Ethernet, LIN, MCAP, DLT logs), sensor data (camera/radar/lidar/GNSS), and video from testing activities conducted at test sites and on public roads.
- Big Data Processing: Clean, standardize, and build reusable data processing pipelines for large-scale data collected from multiple vehicles operating in parallel, ensuring reusability for subsequent testing campaigns.
- System Performance Analysis: Analyze driving scenarios, identify whether ADAS events/alerts are true or false positives, and evaluate system performance based on KPIs such as detection rate, reaction time, disengagement rate, and MRM triggers as defined in the Statement of Requirements (SOR).
- Data Visualization & Reporting: Develop reports, tables, charts, and dashboards to present analysis results to relevant stakeholders, including engineering, program management, and legal/homologation teams, ensuring data is traceable and verifiable.
- Incident Investigation Support: Support root cause analysis when abnormal events or system failures are identified, working with field test engineers to correlate log data with actual driving events.
- Safety Evidence Support: Contribute data and analysis to safety evidence packages, including Scenario Evidence and Fleet Data, supporting Safety Case documentation and homologation processes.
Requirements
3. Mandatory Requirements
- Log Analysis Experience: Experience analyzing CAN logs, ADAS system logs, or vehicle telemetry data; understanding of CAN bus data structures and signal decoding using DBC files.
- Data Analysis Skills: Experience in data processing and analysis, with the ability to present results through clear and intuitive tables, reports, and visualizations that can be easily understood by non-technical audiences.
- Tools & Programming: Proficiency in Python and data processing/visualization libraries such as Pandas, NumPy, and Matplotlib/Plotly; SQL is a plus.
- Education: Bachelor's degree in Data Science, Information Technology, Electrical/Electronics Engineering, Automotive Engineering, Applied Mathematics & Informatics, or a related field.
- Analytical Mindset & Work Attitude: Strong logical thinking, attention to detail, and ability to work independently with large volumes of data while ensuring accuracy and consistency in reporting.
4. Other Requirements
- A valid driving license and hands-on experience using ADAS features are preferred, helping to understand operational contexts when analyzing logs and interpreting driving scenarios from data.
- Experience with automotive data analysis tools such as Vector CANoe/CANalyzer, MATLAB/Simulink, or internal vehicle data analysis platforms.
- Knowledge of big data pipelines or large-scale data processing platforms such as Spark, Databricks, or equivalent for processing large volumes of log data from multiple vehicles operating in parallel.
- Basic understanding of ISO 26262 (particularly Data Recording/EDR) and Safety KPI concepts in the automotive/ADS industry. Knowledge of automotive development processes such as ASPICE, Waterfall, or Agile is a plus.
- Advanced data visualization skills using Power BI, Tableau, or equivalent tools, with experience building interactive dashboards.
