Intern – Marketing Data Science & Experimentation (MoneyLion, FinTech

Gen Digital • New York, NY

7d • Onsite

About The Position

Gen (Norton, Avast, LifeLock, MoneyLion and more) is a global company powering digital freedom in cybersecurity, identity, privacy and financial wellness for nearly 500 million users in 150+ countries. We combine financial empowerment with cyber safety so people can confidently manage and secure their digital and financial lives. We’re scrappy, customer‑driven, and see AI as a teammate. We create room for healthy debate, experimentation and continuous learning, and we value people with different experiences, identities and ideas. MoneyLion, part of Gen Digital, is a leading fintech platform with a consumer finance super app and embedded finance products that help millions of Americans make smarter financial decisions. This role sits in the MoneyLion Marketing & Yield Analytics team, focused on experiments and analytics that improve growth, risk and yield. If you want hands‑on experience at the intersection of fintech, marketing and data science , this role is for you. As an intern on the MoneyLion Marketing & Yield Analytics team, you will: Work on real experiments that influence how we acquire, approve and serve MoneyLion customers. Help design and analyze A/B tests to understand how product, pricing and targeting decisions affect risk, conversion and yield . Use applied causal inference and heterogeneous treatment effect (HTE) methods to go beyond average results and see whic customer segments respond best. Turn quantitative findings into clear, actionable recommendations for marketing, product and risk stakeholders. This is an applied, hands‑on role: you’ll work with data, experiments and business partners—not doing purely theoretical research or deep‑learning projects.

Requirements

• You are a current undergraduate or graduate student (rising junior/senior preferred) in Data Science, Statistics/Math, Econometrics, Computer Science, Applied Mathematics, Economics or a similar quantitative field.
• You are: Curious, proactive and eager to learn in a dynamic, fast‑paced environment. A self‑starter who likes trying new, practical ideas and learning by doing. Comfortable working where data, experimentation and business questions meet. A structured thinker who wants to use data to drive decisions, not do research in a vacuum. This is an applied marketing / data science experimentation internship.
• Great if you enjoy SQL, Python, A/B testing and causal thinking and want to see your work shape real product and marketing decisions. It is not a pure research or deep‑learning‑only role.
• Strong proficiency in SQL for querying and aggregating large datasets.
• Experience with Python (pandas, NumPy, scikit‑learn or similar) for data prep and modeling.
• Exposure to causal inference / experimentation (ATE, HTE, propensity methods, A/B testing).
• Understanding of basic ML workflows and model evaluation (train/validate/test, overfitting, core metrics).
• Understanding of experimental design (randomization, control vs. treatment, statistical significance).
• Comfort working with large datasets and doing feature engineering and segmentation.
• Strong statistical intuition and willingness to question assumptions.
• Ability to translate quantitative findings into clear business recommendations.
• Clear written and verbal communication, including with non‑technical stakeholders.

Responsibilities

• Experiment Test Plan Organization Help structure and document a standardized experiment analysis plan for MoneyLion growth, risk and pricing tests. Create simple, repeatable templates so future HTE analyses are faster and more consistent.
• Double ML Tooling – Leverage & Refinement Assist in refining feature inputs and model specs for double machine learning (DML) analyses. Support basic checks of assumptions and model stability. Document methods so analyses are reproducible and easy to review.
• Heterogeneous Treatment Effect Analysis Select and analyze one live or recent MoneyLion experiment. Use DML to estimate HTE across: risk tiers, score bands, traffic cohorts and, where relevant, external signals. Compare heterogeneous results to the average treatment effect (ATE) to see where we win, are neutral, or see adverse effects.
• Findings & Business Recommendations Translate DML and HTE outputs into clear yield and growth recommendations. Highlight segments with positive, neutral and negative incremental lift. Recommend targeting, routing or pricing adjustments (e.g., where to push harder in marketing, or tighten/relax policies).

Benefits

• Collaborate on impactful, real‑world projects with experienced professionals.
• Get exposure to real‑time business challenges in fintech marketing and risk/yield .
• Join learning sessions, workshops and networking events with leaders across Gen and MoneyLion .
• Receive ongoing mentorship, feedback and career development support.

What This Job Offers

• Career Level: Intern
• Education Level: No Education Listed