Manager - RLDA

Responsibilities \& Key Deliverables

Role Purpose

The role is responsible for leading Road Load Data Acquisition (RLDA) activities across vehicle development projects, ensuring the availability of high\-quality vehicle data for CAE simulations, durability validation, and concern analysis. The position drives data analytics, digitization, AI adoption, process automation, and productivity enhancement initiatives across the BVVD function while ensuring efficient resource and budget management.

Key Responsibilities

Lead Road Load Data Acquisition (RLDA) activities across vehicle development projects to support durability validation, CAE simulations, and concern analysis.

Develop and establish accelerated test cycles through road load data analysis, customer correlation, and validation methodologies.

Drive digitization, process innovation, and automation initiatives to improve operational efficiency and data\-driven decision making.

Lead the deployment and adoption of AI tools, Generative AI solutions, and AI agents to enhance engineering productivity and validation effectiveness.

Ensure availability, quality, and governance of vehicle CAN data, test analytics, automated dashboards, and reporting systems.

Manage data analytics programs to generate actionable insights and support validation, engineering, and business decision\-making.

Oversee resource planning, team capability development, performance management, and succession planning for the function.

Manage budgets, vendor partnerships, procurement activities, and the development of RLDA and testing standards to drive operational excellence and continuous improvement.

Experience

9\-13 years experience

Industry Preferred

Qualifications

BE Mechanical/Automobile

General Requirements

Functional Skills

Strong understanding of vehicle systems and automotive validation processes.

Expertise in Road Load Data Acquisition (RLDA), instrumentation, strain gauging, Wheel Force Transducers (WFT), and Data Acquisition Systems (DAQ).

Knowledge of fatigue analysis, customer correlation, spectral analysis, block cycle development, and nCode tools.

Experience in data pipeline development, including Extract, Transform, and Load (ETL) processes.

Proficiency in Power BI and data visualization tools.

Strong capabilities in test data analysis, interpretation, and reporting.

Understanding of machine learning concepts and applications.

Hands\-on exposure to Generative AI, Large Language Models (LLMs), and AI\-enabled engineering tools.

Familiarity with cloud platforms such as AWS and related data technologies.

Behavioral Competencies

Result Orientation with Execution Excellence

Managerial Effectiveness

Leveraging Human Capital

Collaboration, Agility, Bold (CAB)

Our commitment to Diversity, Equity, and Inclusion

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