Staff Battery Data Scientist

As we scale lithium-sulfur cell production, we need a Staff Battery Data Scientist who can turn cell, process, and quality data into clear answers about what limits performance and yield. You'll work across R&D, process engineering, quality, and manufacturing, and build analytical systems that make learning more repeatable. Responsibilities: - Identify failure drivers in cell, process, and quality data, and turn them into a prioritized list of next steps for engineering and operations - Lead lithium-sulfur analytics, including cell-to-cell variation, capacity fade, formation behavior, and early-life prediction of long-term performance - Clean and analyze high-volume time series data from cyclers, formation equipment, and manufacturing tools, and link it with cell-level quality data to find root causes - Build, validate, and deploy ML models for performance prediction, anomaly detection, and process optimization, and know when a simpler statistical method is the better choice - Design DOE, SPC, and statistical testing that give engineers actionable decisions, and communicate findings, including uncertainty, to technical and executive audiences - Use AI and LLM tooling to automate recurring analyses and build a searchable knowledge base so learnings persist beyond individuals - Mentor junior team members and set standards for analytical rigor and reproducibility Qualifications: - BS/MS/PhD in data science, electrochemistry, materials science, chemical engineering, computer science, or equivalent experience - Minimum of 8 years in data science or analytics, including 4+ years working with battery, electrochemical, or comparable high-volume manufacturing data - Strong Python and SQL skills, with proficiency in the scientific Python and ML stack (pandas, SciPy, scikit-learn, and PyTorch or TensorFlow) - Demonstrated experience with large-scale time series analysis of instrument or tool data, and with SPC, DOE, and root cause analysis in a manufacturing setting - Track record of taking ML models from prototype to production, using modern data platforms (e.g., Databricks, Snowflake) and version control (GitHub) - Hands-on experience applying LLMs or agentic tools (e.g., RAG, MCP integrations) to automate analysis or knowledge retrieval - Adaptable, first-principles thinker who can separate signal from noise, find root causes, and creatively solve problems for a new battery chemistry Preferred Qualifications: - Experience with lithium-sulfur or other next-generation battery chemistries - Familiarity with cell formation, cycling, EIS, and BMS data - Experience building or supporting data pipelines (SQL/Python-based ETL/ELT, Spark, orchestration frameworks) and working with MES systems and their data outputs - Experience with MLflow, Unity Catalog, or similar model and data governance tooling - Experience with Bayesian methods or survival and reliability modeling Compensations Range: The expected base salary range for this position is between $141,100.00 - $211,700.00 The level of pay within the range will depend on a variety of job-related factors that may include location, relevant prior experience and/or education, or particular skills and expertise. In addition to base pay this position is eligible for tier based bonus and equity, healthcare, dental, vision, corporate discounts, paid holidays, PTO and sick time, 401K, employee relocation plan (if applicable)