Analytics Lead, Deposit Fraud Risk

Affirm

1d•$164,000 - $245,000•Remote

About The Position

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. The Fraud team at Affirm Bank works cross-functionally with Machine Learning, Product, Engineering, Operations to combat fraudulent activities to foster a safe platform. This role requires abundant cross-functional partnership. This individual will be the representative of the Credit Risk org, leading the charge on refining our Identity Verification and Fraud processes as well as our Application flow. Additionally, this role will work closely with the Product and Engineering teams to improve our Fraud solution. Come join us in our mission to change consumer finance through better technology, lower costs, and increased transparency while providing the best customer experience.

Requirements

7+ years’ of Analytics experience
Fluent in SQL and Python
A team player with ability to collaborate and influence across many different teams in the organization
Ability to communicate findings and recommendations clearly to both technical and non-technical audiences
Able to thrive in a fast-paced environment and be responsive and available during times of peak fraud activity
Strong time management skills and the ability to manage multiple projects and priorities
Working knowledge of the fundamentals of payment processing and an understanding of industry risk trends, including familiarity with fraud strategy development
Passion to understand how Affirm product works and a curious mindset to help change and make it more effective

Responsibilities

Own fraud performance for depository products, including monitoring fraud losses, approval rates, false positives, customer friction, and other key risk metrics.
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.
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.