Payments Risk Analyst II

What you'll be doing

• Own end-to-end risk controls for ACH payments, scaling fraud prevention processes to keep pace with business expansion.
• Drive detection and mitigation of fraudulent deposit activity by implementing targeting logic against bad actors and high-risk behavior.
• Lead quantitative analysis of fraud performance measuring rule precision and recall, assessing the financial impact of decisions, and surfacing tradeoffs to leadership with clear, data-backed recommendations.
• Partner cross-functionally with Product, Growth, Data Science, Risk ML, and Engineering to develop and iterate on custom risk management strategies, including the transition from heuristic rules to model-driven and synchronous friction controls.
• Execute investigations into anomalous customer and transaction behavior, proactively identifying monitoring gaps and closing them before they become losses.

What we look for in you

• 4+ years of experience in risk, fraud, or payments operations, with at least 2 years focused on payments fraud prevention in a fintech, e-commerce, or financial services environment.
• 2+ years of hands-on experience querying and analyzing large datasets using SQL; ability to independently scope and execute analytics projects end-to-end.
• Demonstrated experience using identity-provider data and fraud-detection tooling to implement and tune risk controls.
• Strong knowledge of ACH payment processing and applicable NACHA rules.
• Proven ability to measure the financial impact of fraud decisions and communicate findings to senior leadership and cross-functional stakeholders.
• Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human-in-the-loop practices to deliver business-ready outputs and drive measurable improvements in efficiency, cost, and quality.

Nice to haves

• Experience mitigating fraud across multiple payment rails (e.g., ACH, cards, real-time payments) or in international markets.
• Familiarity with machine learning models used in fraud detection, or experience partnering with ML teams to operationalize model outputs.
• Understanding of blockchain analytics and/or fraud trends within the cryptocurrency ecosystem.