Tesla Energy Products Field Quality team is looking to leverage data and analytics to ensure the best customer experience and fleet reliability across the Tesla Energy portfolio; Industrial, Residential, Supercharger, and Solar. The Fleet Data Engineer’s primary role is to leverage real-world performance and maintenance data to drive fleet-wide improvements by identifying trends and determining effective strategies to improve key fleet health metrics.
Requirements
- Strong Python, especially in a data analytics/science capacity where pandas/numpy are used heavily (matplotlib/seaborn/plotly/etc.)
- Experience with multiple data architecture paradigms (SQL, NoSQL, Kafka, Spark)
- Knowledge of various data communication protocols (REST API, Websockets)
- Experience with version control (Git)
- Familiarity with continuous integration pipelines (Docker, Jenkins, Kubernetes)
- Strong analytical skills and knowledge of applied statistics including predictive analytics, time series analysis and machine learning.
- Working knowledge of reliability statistics such as Weibull Analysis preferred
Responsibilities
- Perform statistical analysis to quantify reliability risks in the field and identify emerging trends
- Utilize fleet data for diagnostics and prognostics
- Perform correlation studies between manufacturing data and field performance to develop screening tests
- Maintain data pipelines/ tables & applications within the Field Quality team
- Create data visualizations to communicate analysis results and drive decision making
Other
- Currently pursuing a Degree in Computer Science, Physics, Electrical Engineering or related field
- Able to visualize data effectively
- Python (matplotlib/seaborn/plotly/etc.)
- Tableau (or, similar BI tools)
- Any opensource/freeware viz tools (e.g., D3, ggplot)
