You are the owner of an exploratory workstream and not executing against a fixed spec. You define the technical direction, work hands-on in the lab, and your success is measured by the transition of lab-proven concepts into our real-world BMS product pipeline.
Design and evaluate advanced diagnostic algorithms for state estimation and battery health assessment across cell chemistries (NMC, LFP).
Develop novel fault detection and anomaly detection approaches, combining physics-based features with machine learning.
Build validation frameworks that quantify algorithm performance across operating conditions, temperatures, and aging states.
Define data collection requirements like measurement protocols and operating points needed for robust validation.
Collaborate with research partners on joint algorithm development and structure research project with academic partners.

University degree in electrical / control engineering, physics, applied mathematics, or a similar field.
Strong background in battery physics, aging mechanisms, and state estimation fundamentals.
Proficiency in Python for data analysis and scientific computing (NumPy, SciPy, Pandas).
Experience designing and evaluating diagnostic or estimation algorithms for electrochemical systems.
Comfortable working without a fixed playbook; define your own experiments, critically evaluate results, value negative outcomes, and change direction when the data tells you to.
Excellent communication skills in English; proficiency in German is a strong plus.
Experience with electrochemical impedance spectroscopy (measurement, interpretation, modeling).
Experience applying machine learning to time-series or sensor data (classification, anomaly detection).
Familiarity with embedded constraints; awareness of what is realistic on a microcontroller vs. what belongs in the cloud.
Published work or thesis in battery diagnostics, electrochemical methods, or related fields.
Experience with battery test equipment and lab measurement setups.

Battery Science, Data Engineer

Key skills