Data Science Intern, Charging Data Modeling

Job Description

Internship, Charging Data Modeling, Machine Learning Engineer (Fall 2026) | Tesla

The Tone:
This is an internship at Tesla, where the company designs and manufactures electric vehicles, energy storage solutions, and advanced charging infrastructure. This role focuses on developing crucial models and algorithms for Tesla’s global charging infrastructure, directly influencing the customer charging experience. The work is highly impactful, as it also lays the groundwork for the operations of future autonomous vehicle fleets.

The TL;DR
• Role: Internship
• Type: Full-time
• Location: On-site
• Pay: $50.04–$58.84 hourly
• Team: Charging Data Modeling team
• Mission: Develop models and algorithms to enhance the customer charging experience worldwide and design next-generation fleet orchestration strategies.
• Tech Stack: Python, Golang (plus), SQL, NoSQL databases, Spark, Hadoop, streaming data

What You’ll Actually Do
• Extract Insights: Use statistical analysis and large-scale data to identify trends in fleet usage, charging patterns, performance, and customer behavior.
• Develop Algorithms: Design, prototype, and assist in productionizing algorithms that power customer-facing UI features and pricing signals.
• Optimize Infrastructure: Inform the planning, siting, timing, and optimization of Tesla’s EV charging infrastructure using data-driven insights.
• Orchestrate Fleets: Develop charging-aware fleet orchestration algorithms to determine optimal vehicle charging times, locations, and methods.
• Validate Strategies: Contribute to Tesla’s robotaxi simulation platform to evaluate charging strategies, stress-test algorithms, and validate results against real fleet data.

The Must-Haves
• Background: Currently pursuing a Bachelor’s, Master’s, or PhD in a quantitative field such as Math, Economics, Statistics, Computer Science, Operations Research, or Engineering, with a graduation date between December 2026 and December 2027.
• Experience: Strong programming skills with a solid foundation in data structures and algorithms. Demonstrated experience with statistical data analysis and machine learning, including both supervised and unsupervised models. Experience with time-series or geospatial datasets, and experiment design and causal inference methods, is also required.
• Skills: Proficiency in data analysis and modeling using Python, along with expertise in SQL (relational databases) and/or NoSQL databases. Ability to clearly communicate insights to cross-functional teams is essential.
• Bonus: Experience with Golang and collaborative code development, or with Spark, Hadoop, or streaming data. A background in optimization techniques applied to fleet, logistics, or energy problems, and quantitative projects available online (e.g., GitHub, blog posts, papers), are a plus.