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Job description

Ten-Nine creates new materials for new economies. Our proprietary nano-additive, TENIX®, dramatically improves the function of cathodes - the primary driver of battery cost, performance, and greenhouse gas emissions. It’s a simple powder, produced sustainably and domestically, that can be integrated into manufacturing lines for all kinds of batteries, from single-use primary cells to rechargeable EV packs.

Role Overview

The Data Engineer will help build and maintain data infrastructure, analyze battery test and operational data, and work closely with R&D, manufacturing, and quality teams to drive improvements in battery optimization, processes, and performance. This role combines data-driven and AI methods with engineering expertise to model, predict, and optimize battery behavior under real-world conditions.

Key Responsibilities

• Design, build, and maintain data pipelines and data stores (test data, manufacturing data, sensor data, etc.).

• Clean, preprocess, and validate large datasets from battery cycling tests, diagnostics, environmental tests, and field usage.

• Develop machine learning and statistical models to predict metrics such as state-of-charge (SoC), state-of-health (SoH), degradation, thermal behavior, and safety events.

• Perform exploratory data analysis to identify patterns, anomalies, and correlations in battery performance.

• Collaborate with battery engineers, electrochemists, and manufacturing staff to define data requirements and design experiments.

• Implement monitoring tools, dashboards, and visualization to track battery health, production quality, and performance over time.

• Establish guidelines and best practices for data collection, versioning, quality assurance, and metadata management for battery datasets.

• Optimize data flows for computational efficiency and scalability, including high-frequency sensor data, large test datasets, and real-time streaming.

• Integrate battery system simulations and connect with physics-based models for predictive analysis.

Day-to-Day Responsibilities

• Handle data exports in CSV/Excel, small databases, and occasional cleaning tasks.

• Manage manual uploads to LIMS, SharePoint, or similar systems.

• Implement ETL processes (Extract, Transform, Load) for cycling, impedance, and degradation datasets.

• Automate instrument data capture using APIs, DAQ, and related tools.

• Architect and maintain pipelines from diverse data sources (lab instruments, cycling chambers, BMS logs, thermal sensors, MES/ERP systems).

• Create and maintain dashboards for R&D and manufacturing performance monitoring.

• Enforce metadata standards and ensure reproducibility of R&D experiments.

• Conduct trend analysis, regression, and lightweight machine learning projects across multiple datasets.

• Contribute to weekly/monthly reporting and collaborate with both R&D and manufacturing scale-up teams.

• Support predictive modeling, process optimization, and integration with physics-based simulations.

Qualifications Required

• Bachelor’s or Master’s degree in Data Science, Computer Science, Electrical/Chemical/Materials Engineering, Physics, or related field.

• Proficiency in programming (Python, SQL, related tools).

• Experience with machine learning and statistical modeling (regression, time series, anomaly detection).

• Experience handling experimental/test/multivariate data, including cleaning, preprocessing, and dealing with noise/missing data.

• Ability to quickly learn battery systems: cell/module/pack architectures, test protocols (cycling, impedance, thermal), degradation mechanisms, etc.

• Strong communication skills with the ability to translate technical results into actionable engineering insights.

Preferred

• Experience in battery development (test labs, battery manufacturing, or R&D).

• Knowledge of electrochemistry, thermal properties, and aging mechanisms of cells.

• Experience with hardware and sensors embedded in battery systems (data acquisition, signal conditioning).

• Familiarity with cloud platforms (AWS, Azure, GCP), streaming data, and big-data infrastructure.

• Experience designing dashboards, monitoring systems, and real-time reporting solutions.

• Prior work with simulation and modeling tools (battery simulation, thermal models, finite element).

• Experience building automated data capture pipelines from instruments and test equipment.

• Ability to manipulate and analyze large datasets at scale, applying programming expertise to data handling.

• Strong focus on predictive modeling, degradation mechanisms, and optimization.

Seniority/Levels

This role is open to candidates at multiple levels of experience. Consideration will be given to both early-career and experienced professionals with the required skills and interest listed above.

Location

Tulsa, Oklahoma | Global Remote Consideration Available

Why Join Us

• Be part of an innovative startup at the forefront of energy storage, combining materials science with data engineering.

• Contribute directly to the development of sustainable, domestically produced energy solutions with global impact.

• Work closely with world-class scientists, engineers, and industry leaders to shape the future of batteries.

• Grow your career in a fast-paced environment where data-driven insights fuel breakthrough innovation.