Data Analytics Engineer

Responsibilities

Perform vehicle data mining and exploratory analysis using Python and data-science tools; Support event extraction and triage for customer-reported and internal issues; Analyze vehicle signals and logs (CAN, Ethernet) to identify anomalies and performance concerns; Apply structured problem-solving techniques to support root-cause analysis; Support AI/ML validation workflows for ADAS features, including dataset exploration and quality assessment, feature analysis and labeling support, and review of basic ML performance metrics; Assist in identifying data issues impacting ML model behavior (e.g., noise, missing signals, data drift); Build foundational understanding of how ML models interact with embedded ADAS systems; Support embedded-level triage activities, like ECU log analysis, vehicle communication trace interpretation, and signal-to-algorithm correlation; Assist in debugging issues that span vehicle sensors, embedded software, and analytics outputs; Learn ADAS system architecture and data flow from sensor to ECU to analytics to validation; Participate in hands-on vehicle testing and data collection under guidance of senior engineers; Support vehicle instrumentation, logging setup, and data validation; Help ensure collected data meets analysis and validation requirements; Work closely with the algorithm development team, systems and validation engineers, and project and engineering managers; Follow established ADAS V-cycle development, validation, and documentation processes; Contribute to continuous improvement of data analysis tools and workflows; Lead DM&A team for AWS/Azure/other HPCs

Requirements

Bachelor's or Master's degree in a relevant field, such as Data Science, Computer Engineering, Electrical / Embedded Systems Engineering, or Automotive or Systems Engineering; 2+ years of experience and proficiency in Python, vehicle communication protocols (CAN, Ethernet), and data science tools with analytics; Familiarity with MATLAB, machine learning algorithms, DBQL/SQL, and Linux; Hands-on experience with vehicle instrumentation; Proficiency in structured problem solving; Clear understanding of embedded systems fundamentals (ECUs, signals, logs)

Nice to have

Academic or project experience in AI / Machine Learning, ADAS or autonomous driving systems, and vehicle data analytics; Exposure to vehicle communication protocols (CAN, Ethernet), embedded debugging (logs, traces, signal analysis), and MATLAB, basic C/C++, or signal processing; Familiarity with automotive tools such as CANalyzer or CANoe, SIL (Software in the Loop) or simulation environments, and Agile / Scrum development practices

Benefits

Higher education opportunities (Udacity, Udemy, Coursera); Life and accident insurance; Sodexo cards for food and beverages; Well-being program that includes regular workshops and networking events; EAP employee assistance; Access to fitness clubs (T&C apply); Creche facility for working parents