Job Description
- Develop battery data analytics processes and visualizations for vehicle programs.
- Data management and retrieval using Spark, Hive, Hue, and Jupiter Notebooks.
- Data analysis using Python, MATLAB, and R.
- Data visualization with Power BI, Tableau, Matplotlib, and ggplot2.
- Collaborate with battery data analytics and engineering groups on algorithm design, results, modifications, production issues, data consumption methods, and lessons learned.
- Apply analytical methods to assemble insight from electric vehicle usage data. Use the appropriate statistical methods to make actionable inferences.
- Fit complex models to multi-factor datasets using Python, MATLAB, R.
- Model and project the long-term trends of key electric vehicle metrics. Validate results.
- Apply statistical and machine learning models to understand battery production quality metrics.
- Contribute to analytics forums and cross-functional groups by sharing learning within and beyond Global Battery Engineering and Automotive Engineering for PSV operation.
Job Requirement
- Any qualification in Automotive/Electrical/Mechanical Engineering, Computer Science, Applied Mathematics, Statistics, or Data Analytics.
- Minimum technical knowledge of data analysis methods and success in solving complex problems.
- Proficiency using relevant tools. (Python, Spark, Hive, MATLAB, Power BI, R)
- Real-world experience using Hive to query Hadoop data file structures.
- Practical knowledge of machine learning and predictive methods.
- Ability to lead multiple projects and assignments with high level of autonomy and accountability for results.
- Knowledge of high voltage batteries and electrification subsystems.
- Electrochemistry background.
- Willingness to learn and quickly adjust to new tools and systems.
- Capable of converting ambiguous problem statements into concrete project requirements.
- Proven proficiency in statistical analysis techniques.
- Practical knowledge of machine learning and predictive methods.
