Job Description
Develop battery data analytics processes and visualizations for vehicle programs.
Data management and retrieval using Spark, Hive..
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 comp.
lex 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.
Job Description
Develop battery data analytics processes and visualizations for vehicle programs.
Data management and retrieval using Spark, Hive..
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 comp
lex 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.
