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San Francisco, CA

Internship

Onsite

Intern

EV Realty is building the infrastructure backbone for the electrification of commercial fleets. The Data Analytics Intern will assist in developing and maintaining analytics tools that support project siting decisions and customer acquisition, working closely with the Data Science team and other departments.

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Responsibilities

Assist with data pipelines, transformations, and data preparation for integration with EV Realty’s backend infrastructure and tools
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
Assist with LLM and AI innovation initiatives to enhance end-user experiences
Help create niche data products as needed to support internal team objectives
Work with cross-functional leaders and experts to establish best practices and identify opportunities to improve the product development and delivery process
Effectively communicate key insights from analyses, methodologies, and data products to internal teams

Qualification

Required

Currently pursuing a Bachelor's degree in Data Science, Statistics, Computer Science, GIS, Business Analytics or related fields
Junior or Senior standing preferred
GPA of 3.0 or higher
Programming Proficiency and experience in Python, SQL, or similar languages. Python and SQL preferred
Aptitude for GIS-based techniques, data structures, and software such as CARTO, ArcGIS, or QGIS
Analytical experience with Excel
Cloud infrastructure knowledge with Snowflake, Databricks or similar platform
Strong analytical skills and innate problem-solving capabilities
Excellent communication and cross-functional collaboration abilities
Must be comfortable in an unstructured, fast-moving, and constantly evolving high-growth environment
Naturally accountable, responsible, and self-motivated. Independent workflow must be effectively harmonized with remote collaboration