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Lead Engineer - Powertrain Data Analytics
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Responsibilities & Key Deliverables
Key Tasks and Deliverables
- Analyze and interpret vehicle test data from ICE and EV platforms to derive actionable insights.
- Develop and maintain cloud-based analytics methodologies using MATLAB, Python, and Databricks.
- Design and deploy interactive dashboards and web applications for data visualization and reporting using Power BI and Tableau.
- Collaborate with calibration and testing teams to integrate analytics into vehicle development workflows.
- Support predictive modeling and machine learning initiatives for performance and reliability analysis.
Critical Must-Have Deliverables
- Accurate analysis of drivability and performance test data for ICE and EV platforms.
- Development of robust data analytics pipelines leveraging Python, MATLAB, and cloud platforms.
- Creation of real-time dashboards for vehicle performance monitoring and reporting.
- Integration of analytics into calibration and testing processes to improve efficiency and reliability.
Good-to-Have Deliverables
- Implementation of advanced machine learning models for predictive maintenance and performance optimization.
- Development of simulation models for vehicle systems to support virtual testing.
- Contribution to cross-functional projects involving EV efficiency mapping and fleet durability analysis.
Experience
- 3–5 years of experience in engine/vehicle testing or system simulation.
- 1–2 years of hands-on experience in data analytics and visualization.
- Exposure to ICE and EV architectures, calibration tools, and CAN protocols.
Industry Preferred
Automotive OEMs and Tier-1 suppliers specializing in powertrain development, EV systems, and vehicle testing.
Qualifications
- Bachelor’s or Master’s degree in Mechanical Engineering, Automotive Engineering, Electrical Engineering, or Data Science.
- Certifications in data analytics tools (Python, MATLAB) and cloud platforms (Databricks, Spark) are desirable.
General Requirements
Competencies and Skills
Functional Competencies:
- Vehicle and engine performance analysis, drivability assessment, EV efficiency mapping.
- VCU calibration, emission analysis, fleet durability evaluation.
Tools and Techniques Used:
- MATLAB, Python, Databricks, Spark, Power BI, Tableau, Excel.
- Knowledge of CAN protocols and calibration tools.
Behavioral Competencies:
- Strong analytical thinking and problem-solving skills.
- Ability to collaborate effectively with cross-functional teams.
- Adaptability to emerging technologies and agile workflows.
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