Responsibilities

• 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.
• 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).