Salary
💰 $136,000 - $170,000 per year
Tech Stack
BabelNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow
About the role
- Drive data- and model-based innovation in electrolyte formulation for next-generation batteries
- Blend computational chemistry, machine learning, and electrochemical data analysis to accelerate discovery of high-performance materials
- Develop generalizable and predictive models for properties and performance of electrolyte formulations
- Use computational chemistry/atomistic modeling methods to improve electrolyte component/formulation screening
- Analyze electrochemical testing data (cycling behavior, impedance, voltage curves) to extract performance insights and degradation signatures
- Develop battery cell diagnostics and cycle life predictions
- Apply natural language processing tools to screen scientific literature and extract relevant chemical and performance data
- Collaborate with R&D and product teams to ensure electrolyte designs align with customer specifications
- Communicate findings through technical presentations and data reports to drive decision-making in R&D and product
Requirements
- Ph.D. in Chemistry, Chemical Engineering, Materials Science, Physics, or a Data Science-related field
- 3+ years of experience
- Demonstrated experience in data analysis, statistical modeling, and machine learning (experience with molecular graph neural networks is a plus)
- Experience with cheminformatics tools such as RDKit or Open Babel
- Familiar with computational chemistry techniques, such as DFT, MD, or atomistic modeling
- Proficient in Python (pandas, NumPy, scikit-learn, Tensorflow, PyTorch, Bayesian Optimization) and SQL for data manipulation and analysis
- Knowledge of molecular graph neural networks is a strong plus