Data Scientist, Principal Member of Technical Staff

QuantumScape Corporation • San Jose, CA

2d • Onsite

About The Position

QuantumScape is on a mission to transform energy storage with solid-state lithium-metal battery technology. The company’s next-generation batteries are designed to enable greater energy density, faster charging and enhanced safety to support the transition away from legacy energy sources toward a lower carbon future.

About the team: Manufacturing Quality is a diverse group of engineers and data scientists who bring deep expertise in materials science, electrochemistry, statistics, and machine learning. Our team thrives on data-driven decision-making and a shared commitment to drive continuous improvement to QuantumScape’s solid-state battery technology. We work closely with the Manufacturing, Reliability, and R&D, groups to identify failure modes, engineer critical-to-quality specifications, and implement robust control strategies across all process areas.

What we need: The Manufacturing Quality team is seeking a highly motivated principal-level data scientist who is energized about improving the quality and reliability of our solid-state Li metal batteries. As a team member, you will leverage a diverse set of methodologies to detect critical defect modes on our cells and develop robust specifications to be implemented on the manufacturing line. The ideal candidate has a hard sciences background, extensive industry experience, and the ability to communicate complex technical information to a diverse group of stakeholders. If you enjoy problem-solving and thrive in a highly collaborative, fast-paced environment, we’d like to hear from you.

Requirements

• BS degree in Data Science/Statistics/Computer Science/Engineering/Applied Math/Applied Physics. MS or PhD preferred.
• 10+ years of Data Science or Machine Learning experience working on highly complex problems in the fields of Materials Science, Physics, Chemistry, Engineering, or equivalent fields.
• Proficiency with Python data science and machine learning libraries, such as Pandas, Scikit-learn, SciPy, TensorFlow, PyTorch, etc.
• Proficiency with SQL to query data from database and data warehouse storage (i.e.: GCP’s BigQuery)
• Experience deploying machine learning models to production and contributing to the end-to-end lifecycle of an ML project including creating datasets, model training, deployment, and retraining
• Industry or academic experience with statistical analysis for manufacturing processes, like regression (i.e.: linear, logistics), t-tests, comparison of different test groups, survival analysis, etc.
• Experience developing machine learning models such as tree-based models (i.e.: decision trees, random forest, XGBoost, etc.) or deep learning models (i.e.: neural networks, autoencoders, etc.) to predict binary or continuous outcomes using large, complex datasets.
• Ability to communicate findings to colleagues from various disciplines through clear and concise reports, presentations, and data visualizations.
• Familiarity with version control systems like git

Nice To Haves

• Fundamental understanding of battery electrochemistries and failure mechanisms. Familiarity of electrical test and metrics.
• Proficiency with AI coding tools to accelerate data analysis and visualization, model development, etc.
• Proficiency with JMP, Microsoft Office, VSCode, and Github Copilot.

Responsibilities

• Leverage advanced statistical and machine learning techniques to uncover complex patterns in large datasets of battery and component-level electrical test outcomes.
• Build predictive models to identify key features in metrology and microscopy images of cells, separators, and cathodes to predict downstream electrical performance. Propose opportunities to tighten specifications to drive improved product reliability.
• Collaborate with R&D engineers through weekly in-person team meetings to generate new metrics and/or deep learning models to capture defect modes in 2D/3D images.
• Analyze electrical test times-series data to build and improve ML models that identify and predict device failures. Maintain model performance as processes and chemistries change over time.
• Leverage automation tools to execute training jobs in cloud infrastructure and automate pipeline steps like model evaluation and selection. Advance existing processes for subject matter experts to create labels.
• Continuously study state-of-the-art data science methodologies through AI-assisted literature review, critically identify best options, and rapidly apply them to internal projects.

Benefits

• QuantumScape also offers an annual bonus and a generous RSU/Equity package as part of its compensation plan.
• In addition, we do offer a tremendous benefits plan including employee paid health care, Employee Stock Purchase Plan (ESPP), and other benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal