Data Scientist Intern, Supercharger

  • Internship
  • Tokyo, JP
  • Applications have closed

Website Tesla Tesla

EV INFRASTRUCTURE DATA SCIENTIST INTERN

The EV Infrastructure team forms the bridge between Engineering, Service and Deployment teams around EV Charging Infrastructure around the world. With over 1200 Supercharger locations and several thousand destination charging sites around the world, charging infrastructure is a key piece for accelerating an affordable electric mobility solution without compromises.

The Supercharger Analytics Team uses data analysis and machine learning to retrieve actionable insights for the development and enhancement of the charging experience at a global scale. We are looking for a self-starter who instinctively and consistently creates personal and professional stretch goals and meets them to join our global team of data scientists.

Responsibilities

Include, but are not limited to:

  • Work closely with the rest of the EV Infrastructure team to make data-driven decisions
  • Use statistical modeling, supervised learning, and clustering to gather insights on EV infrastructure usage around the world
  • Build a reliable, fast, and dynamic stream of data for analytical purposes
  • Develop visualization tools for geospatial and temporal data sets
  • Develop monitoring KPI’s for tracking network performance

 

Minimum qualifications

  • Currently pursuing a Master’s degree, preferably in a related field (e.g., CS, Operations Research, Software Engineering, Statistics)
  • Excellent analytical thinking and communication skills
  • Strong programming skills with a solid foundation in data structures and algorithms
  • Proficiency in Python and pydata stack (numpy, scipy, pandas, scikit-learn, flask)
  • Experience with statistical data analysis such as linear models and time series forecasting
  • Solid background in statistical learning with experience in using both supervised and unsupervised models
  • Proficiency in SQL relational databases and/or NoSQL databases

Preferred qualifications

  • Master or PhD studies, preferably in a related field (e.g., CS, Operations Research, Software Engineering, Statistics)
  • Experience in an agile working environment
  • Quantitative projects available online (articles, blog posts, github, kaggle)
  • Experience with QGIS, D3, Hadoop, Spark, and/or streaming data