Fraud Data Analyst

Responsibilities

• Architect Real-Time Strategies – You will be responsible for our real-time decisioning engine, designing and deploying logic that combines ML scores with velocity features to make millisecond decisions.
• Precision Rule Development – Translate complex fraud patterns into high-performing rules, optimizing the balance between False Positive Rates (FPR) and Gross Fraud Loss.
• Deconstruct Payment Fraud – Proactively hunt for emerging schemes (ATO, social engineering) and turn these insights into end-to-end mitigation strategies.
• Optimize the Decisioning Stack – Partner with Product and Engineering to define analytical requirements for new features and third-party data signals.
• Calibrate ML & Heuristics – Work with Data Science to operationalize machine learning models, determining how to best "wrap" model scores with heuristic rules.
• Champion Frictionless UX – Analyze security measures to identify unnecessary hurdles, designing dynamic friction strategies for a seamless user experience.
• Strategic Performance Reporting – Monitor rule efficacy and portfolio health, presenting data-driven recommendations to senior leadership to evolve our risk appetite.

Requirements

• Experience – You have between 3 and 7 years of professional experience, specifically within the risk, payments, or credit sectors.
• Technical Proficiency – You are highly skilled in SQL and Python for data analysis and strategy development.
• Domain Expertise – You have a solid understanding of payment fraud dynamics and experience working with real-time decisioning environments.
• Communication – You possess excellent communication skills and are fluent in English.
• Nice to Have – Experience with Machine Learning, Data Science, PySpark, or GraphQL knowledge is a significant plus.