Group Manager - Cell Systems Research & Modeling

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. 

What You'll Do

• 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 characterization data, validated models, and integrated technical insight delivered on program-relevant timelines. 

• Faster and more confident decisions supported by connected experiments, physics-based understanding, AI/ML-enabled analysis, and clear uncertainty assessment. 

• Shorter issue-resolution cycles through stronger diagnostics, root-cause analysis, and focused validation plans. 

• Broad adoption of the team’s methods, data, models, and technical guidance across research & development, engineering, and program teams. 

• Strong team performance, engagement, talent development, and succession readiness. 

Your Skills & Abilities (Required Qualifications)

• PhD in Materials Science, Chemistry, Chemical Engineering, or a related technical field; equivalent relevant experience may be considered. 

• 15+ years of relevant experience in battery cell characterization, electrochemistry, diagnostics, physics-based modeling, simulation, AI/ML-enabled analytics, or a combination of these areas. 

• Demonstrated depth in at least one core discipline and the ability to integrate and lead work across the others. 

• 10+ years of experience leading people, highly technical teams, major technical programs, or cross-functional research efforts, including talent development and performance responsibility. 

• Experience leading cross-functional teams in a matrix organization, including stakeholder alignment, resource coordination, dependency management, conflict resolution, and influence without authority. 

• Demonstrated success setting technical direction and translating research into engineering, product, supplier, or program decisions. 

• Strong communication, collaboration, coaching, feedback, and relationship-building skills, with the ability to work effectively with technical experts, senior leaders, program teams, and suppliers. 

What Will Give You A Competitive Edge (Preferred Qualifications)

• Advanced technical depth in electrochemistry, physics-based modeling, or AI/ML-enabled analytics. 

• Experience developing and validating electrochemical, thermal, mechanical, transport, or degradation models against cell-level data. 

• Experience applying AI/ML to diagnostics, prediction, experiment prioritization, digital engineering, virtualization, uncertainty quantification, or automated decision support. 

• Experience leading organizational growth, transformation, or integration of previously separate technical capabilities, including organizational design and change management. 

• Knowledge of GM technology development, battery validation, supplier assessment, and technology-transition processes. 

• Experience establishing shared technical standards, communities of practice, or capability roadmaps across organizational boundaries.