Affirm is hiring a
Analyst II

Summary

Join us in our mission to deliver honest and transparent financial products that improve lives. The Fraud Strategy and Analytics team works cross-functionally with Machine Learning, Product, Engineering, Operations to combat fraudulent activities and foster a safe platform for our users.

Requirements

• EXPERIENCE - 2-5 years work experience as a data analyst/data scientist in fraud/identity or credit risk at a FinTech company
• TECHNICAL SKILLS - Must be highly proficient in SQL; Experience with Python or R and data visualization tools is a plus; Experience/knowledge with Machine Learning is a plus
• PEOPLE SKILLS - A team player with the ability to collaborate and influence across different teams in the organization
• COMMUNICATION - Ability to clearly communicate findings and recommendations to both technical and non-technical audiences
• PRODUCT KNOWLEDGE - Passion to understand how the product works and how to change to make it more effective
• MULTI-TASKING - Strong time management skills and the ability to manage multiple projects and priorities; Ability to thrive in a fast-paced environment and be responsive and available during high intensity moments
• EDUCATION - Bachelor’s or Master’s degree degree in a quantitatively rigorous discipline like analytics, engineering, statistics, math, or economics
• RISK KNOWLEDGE - Knowledge of the fundamentals of payment processing and an understanding of industry risk trends, including familiarity with risk strategy development

Responsibilities

• Proactively explore data and identify opportunities to improve identity and fraud prevention strategies
• Translate analysis and trends into decisioning logic recommendations to improve identity and fraud conversion rates or reduce fraud losses
• Own end-to-end analytics workflow, including finding the pain points, defining success metrics, socializing ideas across the organization, and monitoring the A/B test
• Use DBT and Looker to monitor the performance of the identity and fraud decisioning systems
• Develop scalable frameworks to manage tiered cutoffs for proprietary fraud machine learning models
• Evaluate new external data sources to solve individual identity risk, device risk and transaction risk
• Partner with Machine Learning and to build fraud strategies for new product and user segments

Benefits

• Health insurance
• Retirement benefits
• Paid time off
• Remote work, flexible hours
• Life and disability insurance
• Bonuses and incentives
• Professional development opportunities
• Wellness programs
• Family and parental leave