Data Engineer/Scientist Internship (Fall 2022)

Tesla

  • Internships
Disclaimer: This position is expected to start around August or September 2022 and continue through the entire Fall term (i.e. through December/January) or into early Spring 2023 if available. We ask for a minimum of 12 weeks, full-time, for most internships. Please consider before submitting an application. 

International Students: If your work authorization is through CPT, please consult your school before applying. You must be able to work 40 hours per week. Many students will be limited to part-time depending on their academic standing. 
Internship Program at Tesla  
The Internship Recruiting Team is driven by the passion to recognize emerging talent. Our year-round program places the best students in positions where they will grow both technically and personally through their experience working closely with their manager, mentor, and team. We are dedicated to providing an experience that allows for the intern to experience life at Tesla by giving them projects that are critical to their team’s success.   
Instead of going on coffee runs and making copies, you’ll be seated at the table making critical decisions that will influence not only your team, but the overall achievement of Tesla’s mission. 
About the team 
Intern will utilize large-scale data and help Tesla engineers design and validate the most compelling and reliable products for our customers. The reliability data team collects real-time life data from test and fleet (energy, charging, and vehicle products) and is responsible for retrieving, analyzing and summarizing results to cross-functional teams. The team provides support through the whole design cycle by building software and statistical tools that orchestrate all the reliability physics analyses. 
Responsibilities 
  • Apply modern statistical frameworks to support Design for Reliability and associated corrective actions 
  • Answer complex questions on fleet usage and behavior to enable proactive monitoring, grow reliability, and minimize field failures 
  • Build scalable data pipelines to deploy fleet health monitoring models 
  • Work closely with cross-functional teams to create/interpret/validate numeric models of fielded and in-test products  
  • Build visualizations to effectively communicate results 
Requirements 
  • Currently working towards Bachelor’s degree or higher in quantitative discipline (e.g. Statistics, Computer Science, Mathematics, Physics, Electrical Engineering, Industrial Engineering) or the equivalent in experience and evidence of exceptional ability 
  • Excellent Software skills (proficiency in Python – Data Science stack) 
  • Strong DevOps skills 
  • Strong knowledge of data structures, architectures, and languages such as SQL  
  • Solid understanding of statistics 
  • Strong verbal and written communication skills 
Nice to have 
  • Experience with Machine Learning & time-series modeling  
  • PySpark and Big Data frameworks 
  • Familiarity with CI/CD 
  • Ability to code robust apps (potentially interfacing with data streams, etc.)