The opportunity
At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most.
What you'll do
Build and maintain scalable data models and transformation pipelines, using: tools (such as DBT) to standardize depository product, transaction, customer, fraud, and operational data.
Define trusted fraud performance metrics and analytical datasets, including: fraud loss rates, approval and decline rates, false-positive rates, customer friction, and operational efficiency.
Develop recurring dashboards, scorecards, and monitoring frameworks to: provide clear visibility into fraud performance, portfolio health, and emerging risks.
Conduct deep-dive analyses to diagnose fraud losses, control gaps, false: positives, portfolio shifts, and unexpected performance changes, translating findings into actionable recommendations.
Develop and optimize fraud rules, policies, thresholds, models, and decision: strategies that balance loss prevention, customer experience, operational capacity, and product growth.
Evaluate new data sources and signals to improve detection of identity risk,: account takeover, device risk, transaction risk, and other fraud typologies relevant to depository products.
What they're looking for
- Lead testing and performance measurement of fraud strategy changes, including: rule launches, model updates, policy changes, and new product controls, with clear pre- and post-implementation evaluation.
- Partner cross-functionally with Product, Engineering, Machine Learning, Data: Engineering, Fraud Operations, and Risk to improve fraud strategies, strengthen operational feedback loops, and support new product launches.
