Intern – Marketing Data Science & Experimentation (MoneyLion, FinTech

Gen Digital Inc.•New York, NY

1d•Hybrid

About The Position

MoneyLion, part of Gen Digital, is a leader in financial technology powering the next generation of personalized products, content, and marketplace technology. This role is a full-time 10-week internship (approximately 40 hours per week) based in the New York City office. The intern will support the Data Science & Analytics team with a strong focus on data engineering and practical analytics work. They will gain hands-on experience with tools like dbt, Feast, Snowflake, and Looker to build and maintain analytical datasets, support feature creation, and deliver dashboards and ad-hoc analyses that inform business decisions across MoneyLion and the broader Gen ecosystem. The internship offers a hybrid work model, with 3 days in the New York City office.

Requirements

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.

Nice To Haves

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.

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.