Hybrid Mid-level Fraud Strategy and Prevention Analyst – ML, Modeling

at PagBank

• Job title: Mid-level Fraud Strategy and Prevention Analyst – ML, Modeling
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Python, SQL, Tableau
• Location requirements: Hybrid, Sao Paulo, Brazil

About the role

• Fraud Strategy and Prevention Analyst at PagBank developing predictive fraud prevention strategies using machine learning techniques and data analysis. Collaborating with technology and product teams to enhance antifraud solutions.

Responsibilities

• Develop and implement predictive fraud prevention strategies using supervised and unsupervised machine learning (decision trees, gradient boosting, clustering, outlier detection).
• Design, evolve and document analytical pipelines and databases that support antifraud decision-making.
• Create, calibrate and optimize operational rules in decision engines using statistical analysis, historical and behavioral data.
• Conduct A/B tests to evaluate the impact of new models, policies and adjustments to decision flows.
• Continuously monitor transactions, credit operations and suspicious events, identifying patterns, anomalies and emerging fraud vectors.
• Perform advanced exploratory and diagnostic analyses to investigate deviations and develop new antifraud hypotheses.
• Work in partnership with Technology, Product and Customer Support teams to implement, monitor and evolve antifraud solutions.
• Identify vulnerabilities in internal flows and propose improvements supported by data analyses and indicators.
• Produce executive reports with strategic insights and recommendations for different levels of the organization.
• Develop, update and maintain standard operating procedures (SOPs) related to fraud prevention.
• Implement and monitor risk controls for individual (retail) and corporate clients, ensuring compliance and ongoing mitigation.
• Contribute to strengthening governance practices by supporting policies, metrics and monitoring routines.

Requirements

• Bachelor's degree in Computer Science, Engineering, Statistics, Economics, Mathematics, Information Systems or related fields.
• Proven experience in fraud prevention and detection at financial institutions, fintechs or technology companies.
• Strong background in data analysis, statistical modeling and machine learning applied to risk and fraud.
• Proficiency in SQL, Python and/or R.
• Experience working with large volumes of data, including data cleaning, extraction and interpretation of complex patterns.
• Experience creating, maintaining and optimizing rules in antifraud/transaction monitoring engines.
• Knowledge of data visualization tools (Power BI, Tableau, Looker, etc.).
• Experience in ongoing evaluation of rule and model performance.
• Advanced MS Office skills.
• Intermediate English.

Benefits

• Meal allowance and/or meal vouchers.
• Health and dental insurance.
• Life insurance.
• Partnerships with TotalPass and ZenKlub.
• Extended maternity and paternity leave.
• Childcare assistance.
• Up to 50% discounts on postgraduate and MBA programs from leading institutions such as FIA, FAAP and PUCRS.
• No dress code: wear what makes you comfortable.
• Birthday day off (#TáDeParabéns).
• Baby Gift for newborns.