Data Science Intern – Fleet Analytics

Job Description

Internship, Data Engineer, Fleet Analytics (Summer 2026) | Tesla

The Tone:
This is an internship at Tesla, located in Palo Alto, CA. Tesla is an organization that builds groundbreaking electric vehicles, Superchargers, and energy storage devices. This role is crucial because data is deeply embedded in Tesla’s product and engineering culture, relying on vast amounts of information to improve products, optimize designs, detect faults, and manage energy loads. The work directly impacts Tesla’s offerings and enables hundreds of engineers across various disciplines.

The TL;DR
• Role: Internship
• Type: Full-time
• Location: In-person, Palo Alto, CA
• Pay: $40–$50 hourly
• Team: The Fleet Analytics team is a central team that helps many other teams leverage the data Tesla collects.
• Mission: This role supports engineers across disciplines by providing data analysis and building tools to self-serve insights, directly impacting Tesla’s product and customer safety.
• Tech Stack: AWS, S3, Spark, Trino, Jupyter notebooks, Pandas, Bokeh, Superset, Airflow

What You’ll Actually Do
• Drive Decisions: Work with stakeholders to refine vague problem statements, scope analyses, and use results to inform decisions.
• Conduct Analysis: Write reproducible data analysis over petabytes of data using cutting-edge open-source technologies.
• Communicate Insights: Summarize and clearly communicate data analysis assumptions and results to various audiences.
• Build Pipelines: Build data pipelines to promote ad-hoc data analyses into production dashboards for engineers to rely on.
• Enable Self-Service: Design and implement metrics, applications, and tools that will enable engineers to self-serve their data insights.
• Engineer Software: Write clean and tested code that can be maintained and extended by other software engineers.

The Must-Haves
• Background: Pursuing a degree in Computer Science, Data Science, or a related field, and graduating in 2026-2027; must be actively enrolled in an academic program.
• Experience: Experience writing software in a professional environment, and experience building data visualizations.
• Skills: Strong proficiency in Python and SQL, ability to understand electrical systems, and a strong foundation in statistics.
• Bonus: Experience with data science tools such as Pandas, Numpy, R, Matlab, or Octave, and experience building data pipelines and web applications.