Senior Manager, Fraud Analytics, Risk

Role Overview

• Own the fraud rule lifecycle — designing, testing, deploying, and continuously optimizing detection rules and thresholds to maximize catch rate while minimizing friction.
• Lead AI-enabled fraud use cases end-to-end: use-case sizing, feature ideation, population analysis, and data quality assessment to ensure solutions are analytically sound and value-accretive.
• Conduct periodic performance reviews of fraud models and rules; drive root-cause investigations to proactively surface risk before it scales.
• Serve as a trusted advisor for AI and ML use cases within fraud, ensuring alignment with enterprise AI governance standards, regulatory expectations, and the AI Use Case Registry.
• Evaluate vendor fraud solutions through an analytical lens — assessing effectiveness, overlap, and value relative to cost and risk-reduction objectives.
• Stay current on AI and automation tools and actively explore how they can enhance fraud detection workflows and efficiency.
• Write and maintain intermediate-to-advanced SQL queries to investigate fraud signals, identify connected accounts and orders, and surface emerging patterns.
• Develop and maintain executive dashboards covering fraud review rates, chargeback performance, model effectiveness, and loss trends.
• Translate complex analytical findings into clear, executive-ready narratives — articulating implications for fraud loss, operational efficiency, and customer experience.
• Proactively identify trends and anomalies at both the individual order and systemic level, communicating the full arc of a problem — from discovery to resolution — to any audience.
• Support daily fraud review operations — account verification, order review, escalation handling, SLA adherence, and QA.
• Serve as an internal escalation point for complex cases; assist in coaching the team and continuously improve review processes.
• Partner cross-functionally with Product, Engineering, Compliance, and Legal to align fraud controls with regulatory requirements and product priorities.
• Manage law enforcement requests as needed.

Requirements

• 4–6 years in fraud analytics, fraud strategy, or payments risk — ideally within fintech, e-commerce, or a marketplace.
• Demonstrated experience designing and optimizing fraud detection rules or model-driven thresholds.
• Strong SQL proficiency — complex queries from scratch, data troubleshooting, and data storytelling; Snowflake or similar a plus.
• Hands-on experience with order-level fraud review, chargebacks, dispute management, and regulatory compliance.
• Proven ability to build risk reporting and stakeholder dashboards, and present findings confidently to both technical peers and senior leadership.
• Experience with AI-enabled workflows and/or an active interest in applying AI to fraud and risk — including responsible, governance-aligned use.
• Self-starter who synthesizes complex information quickly, drives projects forward without heavy oversight, and connects individual cases to systemic patterns.
• Comfortable in a fast-paced environment where priorities shift as the threat landscape evolves.

Tech Stack

• SQL

Benefits

• 401K
• paid time off
• dental
• medical
• vision
• disability
• life insurance options