Risk & Fraud Analytics Analyst

About PayJoy

PayJoy, a Public Benefit Corporation, is a mission-first credit provider dedicated to helping under-served customers in emerging markets to achieve financial stability and success. Our patented technology for secured credit provides an on-ramp for new customers to enter the credit system. Through PayJoy’s point-of-sale financing and card offerings, customers gain access to a modern quality of life. PayJoy’s credit also allows our customers to seize opportunities as micro-entrepreneurs, and acts as insurance for tough times. Through our cutting-edge machine learning, data science, and anti-fraud AI, we have served over 18 million customers as of 2025 while achieving solid profitability for sustainable growth.

This role

We are looking for an exceptional new graduate to join our Risk & Fraud team. In this role, you will use data science and machine learning to underwrite customers and detect, prevent, and mitigate fraud across our platform. You will work at the intersection of machine learning, data, and strategy—building models and decisioning systems that protect customers while enabling responsible growth. This is a high-impact role where your work will directly influence loss prevention, user experience, and business performance.

Work within Fraud & Risk and partner with Product / Engineering teams to translate fraud patterns into scalable ML solutions. Develop and deploy machine learning models to detect and prevent fraudulent activity (e.g., transaction fraud, account takeovers, identity fraud). Analyze large, complex datasets to identify emerging fraud trends and vulnerabilities. Build and improve real-time decisioning systems and risk scoring frameworks. Design experiments and continuously optimize model performance (precision/recall, false positives, latency). Contribute to a feedback loop between model outputs and fraud operations to improve detection strategies.

Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field from equivalent top-tier program. Strong foundation in machine learning, statistics, and data analysis. Proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Experience working with large datasets and tools such as SQL and Pandas. Strong problem-solving skills and ability to think critically about adversarial systems. Demonstrated experience (internships, research, or projects) applying ML to real-world problems.

100% Company-funded health insurance for employee and immediate family Company-funded employee life and disability insurance $2,000 annual Co-working Travel perk $2,000 annual Professional Development perk $340 Monthly Commuter benefit 401k contribution of 3% of salary (up to IRS limits) Headphone benefit, home office equipment allowance and wellness perks Catered lunches 20 paid vacation days, unlimited sick leave