<p>We are the charging-data-modeling team that uses data analytics and machine learning to bridge the engineering, service, deployment and operation of Tesla's charging infrastructure and to enhance the charging experience worldwide.</p> <p>With over 70,000 Superchargers and several thousand destination charging sites around the world, Tesla's charging solution aims to accelerate the world's transition to sustainable energy by enabling electric mobility without compromises.</p> <p>We use large-scale data analysis and machine learning models to decide the deployment of the charging infrastructure in terms of location, timing and quantity. We build algorithms that power the vehicle UI features for enhancing the charging experience while minimizing the charging costs to customers.</p> <ul> <li>Use statistical analysis to extract insights on fleet usage, trends, performance </li><li>Improve data-driven decision making through rigorous data analysis, machine learning modeling and clear communication with stakeholders </li><li>Leverage insights to inform planning and optimization of the EV infrastructure </li><li>Design, prototype, and production algorithms that drives customer UI features, and pricing signals </li><li>Build reliable, fast, and dynamic data tools, and data pipelines </li><li>Degree in a quantitative field (e.g., Math, Statistics, Computer Science, Data Science, Engineering) or equivalent in experience and evidence of exceptional ability </li><li>Strong programming skills with a solid foundation in data structures and algorithms </li><li>Proficiency in data analysis, modeling in Python </li><li>Proficiency in SQL relational databases and/or NoSQL databases </li><li>Experience with statistical data analysis and machine learning </li><li>Background in machine learning with experience in using both supervised and unsupervised models is preferred </li><li>Experience with timeseries or geospatial datasets is preferred </li><li>Experience with experiment design and causal inference methodsb is preferred </li><li>Experience with Spark, Hadoop and streaming data?is preferred </li><li>Quantitative projects available online (github, blog posts, etc.) are preferred </li></ul>