Staff Data Scientist, Fleet Analytics Job at Tesla
What to Expect
What You’ll Do
What You’ll Bring
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, 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 help engineers through direct support by doing data analysis for them and through applications and tools so they can self-serve those analyses in the future. 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, Superset, and Airflow. Our work has a direct impact on Tesla's product, and enables the work of hundreds of engineers across disciplines throughout the company.
We're looking for a talented staff engineer to develop applications which leverage our wealth of device data. These applications will span the full breadth of data engineering, data analysis, and data science activities, taking a first-principles approach to problem solving to inform future hardware and firmware designs, as well as ensuring that our existing vehicles, chargers, and energy devices continue to perform to Tesla's exacting standards. Applications will include full-stack web applications, software frameworks to enable efficient training and modeling, and complex systems which drive action. You will be responsible for bringing these applications from concept to production in collaboration with other members of Fleet Analytics, as well as providing ongoing maintenance and community support. In addition, you will occasionally partner with a team focusing on another discipline (e.g., mechanical engineering, electrical engineering, or firmware engineering), joining a critical project to help them define product requirements, optimize control algorithms, or otherwise improve the product quality.
- Work with stakeholders to develop and maintain complex software systems which elevate the use of device data at Tesla
- Provide guidance to Tesla's data engineering / data science community regarding best practices
- Work with engineers to drive usage of applications and tools
- Write reproducible data analysis over petabytes of data using cutting-edge open source technologies
- Summarize and clearly communicate data analysis assumptions and results
- Build data pipelines to optimize the efficiency and accuracy of analysis work across the company,
- Design and implement metrics, applications and tools that will enable engineers by allowing them to self-serve their data insights
- Write clean and tested code that can be maintained and extended by other software engineers
- Keep up to date on relevant technologies and frameworks, and propose new ones that the team could leverage
- Identify trends, invent new ways of looking at data, and get creative in order to drive improvements in both existing and future products
- Give talks, contribute to open source projects, and advance data science on a global scale
- 7+ Years of Software Development experience in a related field
- Strong proficiency in Python, SQL
- Strong foundation in statistics
- Experience building data visualizations
- Experience writing software in a professional environment
- Strong verbal and written communication skills
- Strong problem-solving skills to help refine problem statements and figure out how to solve them with the available data and from first principles
- Curious and driven to solve complex problems
- Smart but humble, with a bias for action
Nice To Have
- Strong proficiency in Scala
- Understanding of distributed computing, i.e. how HDFS, Spark and Presto work
- Experience with devops tools - e.g., Linux, Ansible, Docker, Kubernetes
- Experience with data processing engines like Apache Spark
- Experience with data science tools such as Pandas, Numpy, R, Matlab, Octave
- Experience with complex hardware systems
- Experience building data pipelines
- Experience building web applications
- Experience building machine learning models in a professional environment
- Experience with continuous integration and continuous development
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