DUTIES: Build expertise in data related to 12V and HV batteries, Power Modes, Vehicle Cockpit Unit, Active and Passive Safety, Body Electronics as GM transitions to Software Defined Vehicles (SDV) with a goal to provide immediate impact to drive vehicle program level decisions, detect and resolve warranty issues and benefit the company’s bottom line. Develop next generation vehicle telemetry related diagnostic and prognostic algorithms, utilize databases to house big data, analyze big data to drive pattern recognition, and advanced algorithm and model development. Research, propose, and architect new solutions with the company’s Big data infrastructure and tools to build automation to find, root cause and show impact analysis of issues. Design, manage, and maintain models (Heuristics based and AI/ML), software, analytical cubes, analytical reports/dashboards. 10% domestic travel required. Hybrid Work Policy - 3 days In-office, 2 days remote - Must be able to report to local office.
REQUIREMENTS: Bachelor's degree in Data Science, Computer Engineering, Electrical Engineering, Computer Science, Software Engineering, or related field of study and Five (5) years of experience as a Data Engineer, Data Analyst, Software Engineer, or related occupation OR Master's degree in Data Science, Computer Engineering, Electrical Engineering, Computer Science, Software Engineering, or related field of study and Three (3) years of experience as a Data Engineer, Data Analyst, Software Engineer, or related occupation. Three (3) years of experience with: Microsoft Excel and Excel-based Visual Basic programming language; Python/R and programming languages including C++, C#, Java, and MATLAB; Software development lifecycle and standard tools including IDEs and Version Control; and Developing products and maintaining them in production. Two (2) years of experience with: Azure Cloud Platform and Databricks; Statistical analysis and modelling using big data; Visualization tools including PowerBI and Tableau; GenAI and LLMs in the automotive industry to automate product health management; 12V and HV battery, vehicle mechatronics and vehicle architectures including software defined vehicles; Large automotive datasets including vehicle telemetry data from vehicle mechatronics, vehicle test tool (NeoVI, VDR) data, Vehicle Management and warranty data for building AI/ML models, automation and visualization tools; and Designing and optimizing large scale, cloud native data pipelines and lakehouse architectures on Azure Databricks—spanning Spark-based ETL/ELT, real time vehicle telemetry processing, data modeling, quality frameworks, visualization, LLM/AI integration, performance tuning, and CI/CD automation—to support high volume automotive analytics and enterprise reporting.
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