Factorial seeking a highly motivated Senior Battery Data Scientist to use state of the art statistical and machine learning techniques to enable early prediction of battery cycle life, battery failure detection, and optimization of extreme fast charging protocols for Lithium Metal and All Solid State batteries. This position will work closely with R&D/cell product development teams to accumulate and analyze cell data and also make recommendations on future experimental protocols.

Job Description

• Development and Implementation of state of the art statistical and machine learning models for cell failure detection
• Development and implementation of early prediction of battery cycle life using Bayesian Optimization and machine learning models
• Lead the analytical direction toward understanding battery performance and degradation
• Research and development of multivariate models for prognostics applications, predictive maintenance
• Support and work with cross functional team to design experimental plans to identify and quantify key model parameters, and improve cell performance by understanding physical meaning correlated with simulation and experiment

Qualifications

• MS or PhD degree in Computer Science, Electrical Engineering, Chemical Engineering, Applied Math, Applied Physics or related area
• 2+ years experience in implementation of statistical/machine learning method
• Strong knowledge in the data-driven predictive model development with application in the energy storage systems
• Preferred to have fundamental knowledge of electrochemistry, experience in lithium metal batteries highly preferred
• Experience with HPC/cloud computing
• Experience in writing efficient codes to analyze large data sets using Python/C/C++
• Experience with AI/ML codes and techniques Tensorflow/PyTorch/Bayesian Optimization