Sr. Fraud Analyst

3 weeks, 4 days ago
Full-time
Senior
Data Science and Analytics
Wave

Wave

Wave is a small business software company that offers a suite of money management tools including invoicing, accounting, credit card processing, payroll, receipt scanning, and personal finance tools. With 2.5 million customers worldwide, Wave provides ...

Internet Software & Services
251-1K
Founded 2010
$80M raised

Description

  • Analyze fraud trends, attack patterns, account activity, payment flows, model outputs, and other risk signals to identify emerging threats and control gaps.
  • Design, tune, test, and evaluate fraud rules, risk scoring strategies, detection logic, alerts, and decisioning controls.
  • Monitor fraud losses, capture rates, false positives, manual review volume, customer friction, and control effectiveness.
  • Conduct root cause analysis of fraud events, escalations, missed fraud, anomalous activity, and control failures.
  • Partner with Product, Engineering, Data Science, Analytics, Compliance, Customer Care, and Operations on fraud control improvements.
  • Identify opportunities to improve AI/ML models, identity verification, behavioral signals, vendor tools, automation, workflows, and real-time detection.
  • Prepare analysis, business cases, recommendations, and executive-ready summaries covering risks, tradeoffs, and expected impact.
  • Support testing, launch, monitoring, and optimization of fraud tools, vendor capabilities, detection strategies, and process improvements.
  • Maintain SOPs, reporting routines, control documentation, and governance materials.

Requirements

  • 5+ years of experience in fraud prevention, fraud detection, fraud strategy, payments risk, digital identity, fintech, banking, e-commerce, tax, or financial services fraud programs.
  • Hands-on experience designing, tuning, testing, and evaluating fraud rules, risk scoring, detection logic, alerts, controls, or automated decisioning workflows.
  • Strong analytical skills using dashboards, Excel, SQL or data-querying tools, model outputs, transaction trends, case analysis, and operational data.
  • Experience monitoring and improving fraud losses, fraud capture, false positives, manual review volume, operational efficiency, and customer friction.
  • Ability to identify root causes, connect patterns across data sources, and translate fraud signals into control recommendations.
  • Experience collaborating with Product, Engineering, Data Science, Analytics, Compliance, Customer Care, and Operations.
  • Working knowledge of fraud tools, vendor platforms, rules engines, risk scoring systems, alerting processes, workflow tools, or real-time decisioning environments.
  • Understanding of AI/ML fraud model outputs, model performance monitoring, feature evaluation, or data-driven operational decisioning.

Benefits

  • Inclusive and accessible candidate experience with accommodations available during recruitment.
  • Diverse and collaborative work environment.
  • Google Gemini may be used for confidential interview note-taking only.

Interested in this position?

Apply directly on the company website

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