Data is deeply embedded in the product and engineering culture at Tesla. We rely on data – lots of it – to improve autopilot, to optimize hardware designs, to proactively detect faults, and to optimize load on the electrical grid. We collect data from each of our cars, robots, Superchargers, and energy storage devices to make these products better and our customers safer.

We're the Fleet Analytics team, a central team that helps many teams leverage the data we collect. We provide direct support for critical projects by modeling behaviors and building insights from the data we collect, as well as building self-service applications and tools to empower Engineers in other domains with data. To do so, we leverage our internal data platform built on top of AWS, S3, Spark, Trino using open source data science tools such as Jupyter notebooks, Pandas, Bokeh, Panel, Superset, and Airflow. Our work has a direct impact on Tesla's products, and enables the work of hundreds of Engineers across disciplines throughout the company.

We're looking for a talented Engineer to identify, compute, predict, and communicate core metrics about our vehicle products, and to drive cross-functional efforts to improve these metrics. This will require a cross-functional skillset that can readily understand our product’s electrical architecture to help identify relevant metrics and how to calculate them, along with sufficient software fundamentals to build reliable data pipelines applications to monitor these metrics. These applications will be leveraged by both executives and engineers to ensure our vehicles continue to perform to Tesla's exacting standards. You will be responsible for bringing these applications from concept to production in collaboration with component design teams and other members of Fleet Analytics, as well as providing ongoing maintenance and community support.