Description
Work Arrangement: This role is categorized as hybrid. This means the successful candidate is expected to report to Warren three times per week, at minimum, or other frequency dictated by the business.
The Role
This role offers the opportunity to work in a collaborative team focused on using physics-based models to improve battery cell and pack design. This role will be focused on using both physical data and data output from virtual models to build coarse tools that run at the speed of design. This role will require a technical and highly motivated individual to lead workgroups of Global Virtual Engineering (GVE) engineers and design architects to develop fast synthesis tools. The role will also focus on using competitive benchmarking of both analysis methods and cell & pack designs to ensure our designs are industry leading. This individual must be motivated to understand the enterprise impact of cell & pack design decisions. The individual should have a solid understanding of battery systems, cells, and battery packs.
What You'll Do
- Accelerate cell and pack design coarse tool development by bringing technical understanding of data science and AI/ML tools
- Act as mentor and technical lead to GVE engineers developing coarse tools with data science and AI/ML
- Individually, design, develop and implement machine learning models and algorithms to improve cell and pack design
- Organize and actively ensure success of workgroups with clear deliverables and timelines for deliverables
- Organize a roadmap for tool improvement prioritizing features and enhancements
- Collaborate with design architects to prioritize feature improvements to ensure tool adoption and incorporate customer feedback
- Create and implement a tool release process with tool maintenance procedures to ensure model accuracy improvement as new data becomes available
Additional Description
Your Skills & Abilities (Required Qualifications)
- 3 years of experience building AI/ML models having enterprise impact
- 5+ years of experience with virtual analysis
- BS in Computer Science, Chemical, Electrical, or Mechanical Engineering Required
- Knowledge of AI/ML & data analytics methodologies, trends and technologies
- Strong organizational and leadership skills
- Strong communication and persuasion
- Solid understanding and working knowledge of fundamental physics, equations and numerical methods underpinning all software used
What Will Give You A Competitive Edge (Preferred Qualifications)
- Advanced degree (MS or greater) in Chemical Engineering, Electrochemical Engineering, Materials Engineering, Mechanical Engineering, Computer Science Engineering
- Experience in multiple CAE domains
This job may be eligible for relocation benefits
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