Senior Data Analyst, Partnerships

About the role

Brigit is a holistic financial health company helping everyday Americans build a brighter financial future. With a business model aligned with customers, Brigit creates transparent, fair, and simple financial products that put money back in members’ hands, help them spend wisely, avoid unfair fees, and build credit quickly. The company values autonomy, ownership, and meaningful input, and its work has been recognized by Built In (2026 Best Midsize Companies to Work For in NYC, 2025 Best Places to Work In NYC, 2024 & 2025 Best Startups to Work For In the U.S., 2023‑2025 Best Startups to Work For In NYC), Fast Company’s Most Innovative Companies of 2022, Business Insider’s Most Promising Consumer Startups 2022, and Forbes Fintech 50 2022.

In 2026 Brigit launched Instant Cash as an embedded product for Experian’s consumers. The partnership grew from a small pilot to open availability in six months and is now one of the largest sources of new cash‑advance volume at Brigit. This Senior Data Analytics role sits in the Instant Cash Partnerships pod alongside Product and Engineering. Day‑to‑day responsibilities include analyzing repayment performance of the Experian portfolio, evaluating underwriting and credit‑limit changes, modeling cohort LTV and unit economics, and investigating the funnel from account creation through first advance. The role also owns the Experian analytics layer: dbt models, data definitions, reporting, and data quality monitoring relied on by the pod, Finance, and Experian partners. The ideal candidate has risk or portfolio analytics experience on a consumer credit or earned‑wage‑access product and is comfortable being the sole analyst in a cross‑functional pod with Product, Engineering, and Partnerships.

Responsibilities

• Own delinquency and non‑payback reporting for the Experian portfolio: vintage curves, delinquency roll rates, gross and net loss, and recoveries
• Evaluate underwriting model and credit limit strategy changes end to end: size the population, model the approval / loss tradeoff before launch, and read out actual vs. modeled performance after
• Analyze the collections lifecycle (scheduled repayments, retry flows, charge‑offs, etc) and recommend changes that improve repayment rates without degrading member experience
• Build and refine alerting to detect anomalies quickly, respond to