Risk & Fraud Analytics Analyst

About the Role

PayJoy is 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.

Key Responsibilities

• 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

Requirements & Tech Stack

• 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