Transform complex experimental and testing datasets into actionable insights that drive the autonomous labâs decision-making.
Requirements
- Proficiency in Python (pandas, NumPy, scikit-learn) and SQL for data manipulation and analysis
- Hands-on experience building ETL workflows using tools like Airflow, Prefect, or similar
- Strong foundation in experimental design, statistical inference, and multivariate analysis
- Familiarity with data visualization libraries (Plotly, Dash, or similar) and dashboard frameworks
- Experience working with electrochemical or materials characterization data
- Materials-specific python libraries (pymatgen)
- Exposure to cloud-based data platforms (AWS, GCP, or Azure) and scalable storage solutions
Responsibilities
- Design and maintain robust ETL pipelines to ingest, validate, and preprocess data from diverse sources
- Perform domain-relevant data transformations, extract meaningful descriptors from raw data and develop statistical or machine learning models
- Create interactive dashboards and reports to communicate trends, anomalies, and key insights to scientific and engineering teams
- Collaborate with ML scientists to integrate analytical outputs into active learning loops
- Establish best practices for code versioning, data provenance, and analysis notebooks
- Contribute to internal knowledge bases and publications
Other
- Masterâs or Ph.D. in Data Science, Statistics, Materials Science, Chemistry, Physics, or a related quantitative field
- 2+ years of experience in data analysis, statistical modeling, or machine learning
- Inclusive mindset and a diversity of thought
- Ability to work in unstructured and creative environments
