About EnerVenue, Inc.

Named one of the world’s Top Greentech Companies by TIME in 2024 and 2025, EnerVenue builds simple, safer, and flexible energy storage solutions for the clean energy revolution. Based on technology proven over decades under the most extreme conditions, EnerVenue offers a differentiated stationary storage solution that improves upon today’s alternatives.

Commercialized from research completed at Stanford University and backed by well-funded investors, EnerVenue is redefining the stationary storage market.

About the role

• EnerVenue is seeking a technically exceptional Head of Battery Performance Analytics to build and lead the data and AI capability that turns cycling, characterization, and manufacturing data into engineering decisions across the company's Aqueous Metal Cell (AMC) platform. This is a rare opportunity to build a world-class performance analytics function from the ground up inside one of the most well-funded non-lithium battery companies in the world.
• The successful candidate will own the full performance-data lifecycle — from test data ingestion and pipeline design, through statistical and machine-learning analysis, to AI-assisted decision support for R&D and manufacturing — and will establish a new analytics function at the Changzhou facility. This role sits at the intersection of data science, electrochemistry, and industrial manufacturing, and requires someone equally comfortable building ML models and communicating findings to battery engineers and executives.

What you'll do

Battery Performance Analytics & Data Science

•     Own end-to-end analytics of cell and electrode test data — cycling, EIS, capacity fade, calendar and cycle aging, and abuse/safety testing — across the AMC platform

•     Build and lead AI/ML-driven models for cycle-life prediction, degradation-mode classification, and early failure detection

•     Design automated pipelines to ingest and normalize data from cyclers (Arbin, Maccor, Neware), characterization equipment (SEM, XRD, EIS, BET, XPS), and manufacturing test stations

•     Apply anomaly detection, clustering, and statistical process control (SPC) to catch quality and process drift before it reaches the field

•     Partner with Electrode and Cell Engineering to correlate design and process parameters (foam substrate, plating chemistry, active material loading) with performance outcomes using ML

•     Use generative AI / LLM-based tools to accelerate root-cause analysis, automate reporting, and give engineers a natural-language interface into performance data

Analytics Infrastructure & Team Build-Out (Changzhou, China)

•     Design and stand up a battery performance analytics platform and data warehouse at the Changzhou facility, from blank spec to production-grade deployment

•     Select and implement the analytics stack: Python/R, SQL, cloud or on-prem data infrastructure, and dashboarding/visualization tools (e.g., Power BI, Tableau, Grafana)

•     Establish data governance, quality standards, schema design, and version control for high-volume test and manufacturing data

•     Integrate analytics tooling with MES and production-line sensor/IoT data for real-time quality monitoring

•     Build and lead a China-based analytics team of 5–15 people across data science, data engineering, and battery test/characterization roles

•     Ensure all data infrastructure and vendor tooling comply with relevant Chinese data-security and cross-border data-transfer regulations

Technical Leadership & Cross-Functional Collaboration

•     Serve as the internal authority connecting AI/analytics methods to electrochemistry, materials, and manufacturing engineering

•     Partner with Electrode Engineering, Cell Engineering, and Manufacturing Quality teams to translate performance signals into design and process changes

•     Collaborate with the Stanford-based and Silicon Valley R&D teams to scale analytics approaches from lab-scale datasets to gigafactory-scale production data

•     Establish and manage vendor relationships for battery analytics software, cloud infrastructure, and AI/ML tooling

•     Report directly to the CTO; provide regular updates to executive leadership and investors on performance trends, yield, quality, and analytics roadmap progress

Qualifications

Education

•     Ph.D. or Master's degree in Data Science, Electrical Engineering, Materials Science, Chemical Engineering, Computer Science, Statistics, or a closely related field

•     Equivalent industry experience (5+ years) with a strong portfolio of applied analytics/ML on physical or electrochemical systems will be considered in lieu of an advanced degree

Core Technical Experience (Non-Negotiable)

•     Deep hands-on expertise applying AI/ML to battery, electrochemical, or comparable sensor/time-series performance data

•     Strong proficiency in Python (pandas, scikit-learn, PyTorch or TensorFlow), SQL, and statistical/experimental-design methods

•     Direct experience working with battery test equipment data formats (Arbin, Maccor, Neware, BioLogic) and cycling/EIS analysis

•     Working understanding of battery degradation mechanisms (capacity fade, impedance rise, electrode/interface aging) sufficient to translate physics into meaningful model features

•     Experience building and operating data pipelines/ETL for high-volume, high-frequency time-series test or production data

•     Track record of deploying analytics or ML models that materially influenced engineering, quality, or manufacturing decisions at scale

•     Fluent, current user of AI tools (LLM copilots, code-generation assistants, AI-driven analytics platforms) in day-to-day engineering and analytics work

Manufacturing & Operations Experience

•     Experience implementing analytics, MES, or SPC systems in a Chinese industrial/manufacturing setting

•     Familiarity with IoT/sensor data integration from production lines and industrial equipment

•     Experience managing cross-functional data and analytics teams in a high-growth manufacturing environment

Language & Location

•     Professional working proficiency in Mandarin Chinese required (technical documentation, vendor negotiations, team management)

•     Professional proficiency in English required (communication with Silicon Valley HQ, investor reporting, international publications)

•     Based in or willing to relocate to Changzhou, Jiangsu Province, China

Preferred Qualifications

•     Prior experience in the battery, EV, or energy storage industry specifically

•     Hands-on experience deploying generative AI / LLM tools for engineering copilots, automated root-cause analysis, or report generation

•     Familiarity with cloud data platforms (AWS, Azure, Alibaba Cloud) and MLOps practices for production model deployment

•     Publications or patents in battery analytics, predictive maintenance, or physics-informed machine learning

•     Experience with digital twin or physics-informed ML approaches to battery performance modeling

•     Network within the Chinese battery, EV, or industrial-AI talent pool