Evrealty Us

Evrealty Us

Intern, Data Analytics

San Francisco, CA · Intern · Internship

Sponsorship not specifiedDetected 61 days ago
PythonData StructuresSQLSnowflakeDatabricksCloud PlatformsData AnalysisData EngineeringData ScienceLLMsStatisticsBusiness DevelopmentExcelResearchGISCommunicationCollaborationProblem SolvingAdaptability

About the role

  • The Data Analytics Intern will work primarily with the Data Science team but may require cross-collaboration with our Business Development, Real Estate, and Operations teams.
  • This role will be located in San Francisco, CA or Salt Lake City, UT.

Responsibilities

  • The Data Analytics Intern is a summer position which will contribute to various projects that support EV Realty's analytics tools and processes.
  • Develop Python/SQL scripts for data analysis, automation, and supporting analytical workflows
  • Query SQL databases and leverage GIS to support business analytics and reporting needs
  • Document analytical workflows, processes, data definitions, and research in a clear and effective manner
  • Help create niche data products as needed to support internal team objectives
  • We develop, own, and operate purpose-built

Requirements

  • Currently pursuing a Bachelor's degree in Data Science, Statistics, Computer Science, GIS, Business Analytics or related fields
  • Analytical experience with Excel
  • Must be comfortable in an unstructured, fast-moving, and constantly evolving high-growth environment
  • GPA of 3.0 or higher

Nice to have

  • Programming Proficiency and experience in Python, SQL, or similar languages.
  • Python and SQL preferred
  • Familiarity with the transportation, energy or broader infrastructure sectors is a plus but not required
  • Junior or Senior standing preferred

Company info

  • The Company At EV Realty, we're building the infrastructure backbone that powers the electrification of next generation commercial fleets - one high-powered charging hub at a time.

This listing is sourced directly from Evrealty Us's careers page and normalized into a canonical job model.