Senior Member of Technical Staff, Failure Analysis - Data Science

Quantum Scape 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.

The Cell Development team at Quantum Scape connects failure and reliability data to the specifications that define good material, good process, and good cells. The team owns the analyses linking upstream material, film, and process measurements to downstream cell performance and failure modes, and turns those links into the specs, acceptance criteria, and control limits by which new materials, process changes, and tools are qualified and released into development and production.

The team works in close partnership with Failure Analysis, Process Development, Manufacturing Quality, and Reliability, and prides itself on defensible, evidence-driven decisions.

As a Data Scientist within Failure Analysis, you will turn failure and reliability data into the specifications that define good material, good process, and good cells. You will connect upstream material, film, and process measurements to downstream cell performance and failure modes, and use those links to set data-driven specs, acceptance criteria, and control limits. Your work will directly shape how new materials, process changes, and tools are qualified and released into development and production.

You will play a pivotal role in defining the specifications that take solid-state battery technology from development to production, with opportunities for professional growth and the chance to contribute to high-impact projects. If you are ready to make a significant contribution to the future of the energy economy, we'd like to hear from you.

• Build and maintain analyses that link upstream material, film, and process metrics to cell performance, reliability, and failure outcomes.
• Define and justify specifications, acceptance criteria, and control limits for materials, intermediate products, and cells, grounded in failure and reliability data.
• Design and analyze qualification experiments (splits, controls, and matched comparisons) to decide whether a process or material change is equivalent to baseline.
• Set statistically sound sample sizes, test durations, and pass/fail criteria for qualification and change-control decisions.
• Develop statistical and machine learning models to predict failure rates, find leading indicators of failure, and flag anomalies early.
• Partner with Failure Analysis, Process Development, Manufacturing Quality, and Reliability teams to identify failure modes and root causes, and turn findings into spec updates.
• Maintain traceability between material or process variants and downstream results, so specs can be tightened, relaxed, or reverted as evidence builds.
• Communicate findings and spec recommendations clearly to technical and non-technical stakeholders, including leadership.

• PhD in Data Science, Statistics, Materials Science, Chemical Engineering, Electrical Engineering, or a related field, or an MS with 3+ years of relevant industry experience.
• 3+ years applying data analysis to engineering, manufacturing, or reliability problems. Battery or other advanced-manufacturing experience is strongly preferred.
• Strong grounding in statistics: design of experiments, hypothesis testing, equivalence testing, regression, and reliability or survival analysis (for example, Weibull).
• Proficiency in Python or JMP, and in SQL for working with large, multi-source datasets.
• Ability to turn ambiguous engineering questions into clear, testable analyses…