About the role
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 The Fraud Analytics Analyst is responsible for leveraging data analytics to set a fraud strategy that serves as a competitive advantage, providing the right balance between a seamless user experience and great fraud controls. \n Key
Responsibilities Analyze large datasets to identify trends, patterns, and indicators of fraud, focusing on both broad fraud trends and specific areas such as credit card and transactional fraud. Develop and refine fraud detection models and algorithms to improve detection rates and false positives. Drive research projects through all its phases, from inception to deployment and impact assessment. Prepare and present analytical reports on fraud trends, findings, and the effectiveness of fraud prevention strategies. Write queries, design, build and maintain dashboards and visualizations to monitor randomized experiments and key metrics. Continuously quantify the impact in gross margin of fraud initiatives, size the economic opportunity of product vulnerabilities and process inefficiencies. Work cross functionally with stakeholders in Data Science, Fraud Operations, Product, and Go-to-market.
Qualifications Bachelor’s degree in applied quantitative field, such as statistics, economics, mathematics, etc. 1-2 years of work experience with transactional fraud in credit lending and credit cards, working in the telecom industry and a fast-paced and rapidly changing environments. Strong SQL competencies, and experience with statistical programming languages such as R or Python. Excellent written and oral communication skills, summarize and communicate technical analysis to broad audiences. Strong analytical skills and who are curious by nature. Results-oriented and feels comfortable working both autonomously and in heavily cross-functional environments, taking full ownership of his/her own projects.
