Data Analytics

OnePay

2d•$130,000 - $170,000•Remote

About The Position

OnePay is the consumer fintech trusted by millions of Americans to make money better. Our financial system is broken. High fees, low rates, and too few ways to actually grow your money. We’re fixing it. And we’re moving fast. We’re an all-in-one financial services platform that brings together banking, high-yield savings, credit cards, point-of-sale lending, investing, and crypto in one place. We also partner with employers, HCM providers, gig platforms, and others to deliver embedded financial services to millions of employees and frontline workers. We’re backed by Walmart, the world’s largest retailer, and Ribbit Capital, one of fintech’s most respected investors, giving us rare scale, distribution, and the opportunity to build something truly category-defining. But what really sets OnePay apart is how we move. Our customers don’t have time to wait… and neither do we. This place moves fast, and we’re looking for people who are: Ready to run, Hungry and driven by urgency, Exceptional at what they do, with low ego, Comfortable operating in motion.

Requirements

4-7 years of professional experience in analytics with a proven track record of leveraging quantitative analyses to drive business or product changes in a fast-paced environment.
Demonstrated experience across consumer underwriting strategy and/or portfolio risk analytics.
Advanced SQL skills with a track record of extracting and analyzing complex datasets independently.
Strong analytical thinking and problem-solving skills — you can go from ambiguous questions to clear insights.
Proven ability to work cross-functionally to translate complex data questions into practical business guidance.
Intellectual curiosity to look beyond the ‘what’ and answer the ‘why’.
An act-like-an-owner mentality. We have a bias toward taking action.

Responsibilities

Recommend underwriting, line/limit, and policy changes to responsibly grow approvals and usage (e.g., cutoffs, segmentation, decision logic), using internal behavior, external risk signals, and reject inference techniques.
Develop cross-product customer credit profiles by connecting external credit attributes to on-us behavior, and identify signals that generalize across products vs. are product-specific.
Drive outcomes from uncovering insights in monitoring, to recommending and executing against strategic insights.
Design, execute, and analyze experiments (A/B tests, holdouts, policy tests), turning results into strategic recommendations and roll-out plans.
Intertwine analytics with business and product strategy to craft business proposals that ultimately impact the bottom-line.
Dig into user behavior and “double-clicks” of the data to identify high-impact opportunities for product improvements or targeted outreach, including second-order impacts.
Leverage AI-powered tools and techniques to accelerate analysis, develop and validate predictive insights, and push the boundaries of what’s possible in product analytics.
Share business insights and product enhancement recommendations to senior leadership and stakeholders effectively and concisely.