Group Manager - Cell Systems Research & Modeling

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.   Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale. Group Manager – Cell Systems Research & Modeling   The Group Manager will lead the strategy and execution of cell systems research spanning experimental characterization, physics-based and empirical modeling, and the practical application of AI/ML to experiments and analysis. This role will convert high-quality cell data and mechanistic understanding into predictive insights that accelerate technology evaluation, inform supplier and design decisions, improve failure analysis, and guide development from materials through full cells across vehicle , energy-storage and other battery cell programs.   The successful candidate will bring the technical depth and leadership needed to deliver these outcomes, including expertise in fundamental electrochemistry, battery materials and cell modeling, data science, and AI/ML. They will demonstrate exemplary people leadership, sound technical judgment, and strong cross-functional influence while operating effectively in a matrix organization and collaborating across R&D, engineering, vehicle and energy-storage programs, and suppliers.   This position requires full-time on-site work.

Set the strategy, priorities, resource plans, and execution roadmap for cell characterization and physics-based and empirical modeling across materials, components, prototype cells, and production-relevant formats.   Build and lead a high-performing multidisciplinary organization through hiring, coaching, development, feedback, performance management, recognition, and succession planning. Foster a culture of accountability, inclusion, trust, and continuous learning.   Lead cross-functional teams across characterization, modeling, cell design, materials, safety, manufacturing, engineering, and program organizations. Clarify roles, interfaces, decision rights, dependencies, and shared accountability.   Build trusted partnerships, align stakeholders with competing priorities, resolve conflicts constructively, and communicate technical conclusions clearly to experts, senior leaders, program teams, and suppliers.   Build and sustain characterization capabilities including cell performance testing, three-electrode methods, electrochemical diagnostics, impedance, thermal and mechanical response, and failure analysis.   Lead development and application of models spanning electrochemical, thermal, mechanical, transport, and degradation behavior. Use model outputs to prioritize experiments, quantify tradeoffs, reduce uncertainty, and accelerate program decisions.   Define the material parameters and data needed for modeling and AI/ML integration, including the required personnel, instrumentation, measurement methods, and workflows.   Establish rigorous practices for parameterization, calibration, validation, uncertainty assessment, data curation, model quality, traceability, and responsible AI/ML use. Leverage existing Databricks capabilities and promote repeatable workflows across teams and chemistries.   Develop a model- and data-driven decision system that identifies informative experiments, accelerates learning cycles, and makes uncertainty and confidence visible to decision makers.   Provide actionable technical insight for technology evaluation, supplier assessment, technology down-selection, technology transfer, and vehicle and energy-storage programs.   Success Measures   High-quality