Heading

New York, NY

Internship

Hybrid

$30/hr - $30/hr

Intern

Gen is a global company specializing in cybersecurity, identity, privacy, optimization, and financial wellness. They are seeking a Data Science & Analytics Intern to support their team with a focus on data engineering and analytics work, providing hands-on experience with modern tools to build and maintain analytical datasets and deliver insights that inform business decisions.

Apply Now

Responsibilities

Collaborate with data scientists, analysts, and data engineers to understand data needs, metrics, and business questions
Develop and maintain SQL queries to extract, join, and aggregate data from our Snowflake data warehouse
Assist with data ingestion, cleaning, and preprocessing for analytics use cases, including building dbt models and tests for core transformations
Help define, populate, and maintain feature tables in Feast to support analytics and downstream machine learning workflows
Perform exploratory data analysis (EDA) in Python (pandas, NumPy, notebooks) to profile datasets, identify trends, and surface data quality issues
Build or refine Looker explores, Looks, and dashboards that communicate results clearly and enable stakeholder self-service
Document datasets, dbt models, Feast feature definitions, and analysis steps to ensure reproducibility and knowledge sharing across the team
Participate in regular stand-ups, sprint reviews, and code or query reviews to gather feedback and iterate on work
Occasionally support simple modeling tasks (e.g., feature engineering, train/test splits, basic evaluation) under guidance, while keeping primary focus on data engineering and analytics

Qualification

Required

Currently enrolled in a Bachelor's or Master's program in Computer Science, Data Science, Statistics, Engineering, or a related field
Experience (through coursework, projects, or prior internships) using SQL and relational data modeling (tables, joins, primary/foreign keys)
Experience using Python for data manipulation and analysis (e.g., pandas, NumPy, Jupyter)
Exposure to working with real-world datasets, including dealing with imperfect or messy data
Working knowledge of SQL and relational database concepts
Proficiency in Python for data analysis (pandas, NumPy, notebooks)
Familiarity with at least one BI or data visualization tool, with strong interest in learning Looker if not already experienced
Ability to reason about data quality issues, edge cases, and trade-offs when working with real-world data
Strong written and verbal communication skills and willingness to ask clarifying questions
Ability to thrive in a fast-paced, high-tech environment and manage complex problems
Curious, proactive, and eager to learn new tools and concepts in data engineering and analytics
Collaborative mindset with a willingness to take feedback and iterate quickly
Organized and detail-oriented, with a focus on producing reliable, well-documented work

Preferred

Experience with Snowflake or another cloud data warehouse (BigQuery, Redshift, etc.)
Exposure to dbt (models, tests, documentation, or dbt Cloud/CI)
Exposure to a feature store such as Feast or interest in learning feature-store concepts
Experience building dashboards or explores in Looker, including any LookML basics
Familiarity with Git-based workflows (branches, pull requests, code review)
Basic understanding of statistics or introductory machine learning concepts