Data Analytics Engineer

Overview

Data Analytics Engineer role supporting vehicle data mining, analytics and AI/ML validation for ADAS features within the DM&A team.

What you'll do

• 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, such as 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.

What you'll need

• 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 experience and proficient in Python, Vehicle Communication protocol (CAN, Ethernet), Data Science Tools with analytics.
• Familiar with MATLAB, Machine Learning Algorithms, DBQL/SQL, Linux.
• Hands-on experience with vehicle instrumentation.
• Proficient 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, Agile / Scrum development practices.

Details

• Location: Bangalore, India.