Wave HQ
Sr. Fraud Analyst
- Compensation
- 70.4K–105.6K CAD / yr
- Location
- CA
- Arrangement
- Remote
- Employment type
- Full-time
- Level
- Senior
- Posted
- 14 September 2026 (16 days ago)
Checked 15 days agoApplications go to the employer, never to RoleSprint
About this role
At Wave, we help small businesses to thrive so the heart of our communities beats stronger. We work in an environment buzzing with creative energy and inspiration. No matter where you are or how you get the job done, you have what you need to be successful and connected. The mark of true success at Wave is the ability to be bold, learn quickly and share your knowledge generously.
At Wave, we help small businesses to thrive so the heart of our communities beats stronger. We work in an environment buzzing with creative energy and inspiration. No matter where you are or how you get the job done, you have what you need to be successful and connected. The mark of true success at Wave is the ability to be bold, learn quickly and share your knowledge generously.
About the team: The Risk team is responsible for assessing the credit risk of new businesses, detecting and preventing fraud, optimizing our transaction processes, assisting with enhancements of risk prevention strategies and liaising with our small business owners to develop and implement the best possible solutions.
About the role: This position isn't for the faint of heart: You will be on the front lines identifying fraud and risk flags, making decisions every day to protect our customers and our business. You must be a highly motivated individual with strong communication skills, carry a sense of tact and empathy under pressure, and have a sixth sense for identifying problems while implementing and communicating solutions.
Here's how you will make an impact: • Analyze fraud trends, attack patterns, account behavior, transaction 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 to improve fraud prevention and detection outcomes
• Monitor fraud performance metrics, including fraud losses, fraud capture, false positives, manual review volume, customer friction, and control effectiveness, and recommend improvements based on measurable results
• Conduct root cause analysis on fraud events, escalations, anomalous activity, missed fraud, and control failures, translating findings into specific corrective actions
• Partner with Product, Engineering, Data Science, Analytics, Compliance, Customer Care, and Operations to implement fraud control enhancements and improve real-time detection capabilities
• Support the fraud capability roadmap by identifying opportunities to improve rules, scoring, AI/ML model performance, identity verification, behavioral signals, vendor tooling, automation, and workflows
• Prepare clear analysis, recommendations, business cases, and executive-ready summaries that explain fraud risks, tradeoffs, expected impact, and required actions
• Support testing, launch, monitoring, and optimization of new fraud tools, vendor capabilities, detection strategies, and process improvements
• Maintain SOPs, reporting routines, control documentation, and governance artifacts that support consistent fraud prevention and detection execution
You Thrive Here By Possessing the Following: • 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 strategies, detection logic, alerts, fraud controls, or automated decisioning workflows
• Strong analytical capability using dashboards, Excel, SQL or data querying tools, model outputs, transaction trends, case analysis, and operational data to identify fraud risks and recommend actions
• Experience monitoring and improving fraud performance metrics such as fraud losses, fraud capture, false positives, manual review volume, operational efficiency, and customer friction
• Demonstrated ability to identify root causes, connect patterns across data sources, and translate complex fraud signals into specific control recommendations
• Experience partnering with Product, Engineering, Data Science, Analytics, Compliance, Customer Care, and Operations to implement fraud control improvements
• Working knowledge of fraud tools, vendor platforms, rules engines, risk scoring systems, alerting processes, workflow tools, or real-time decisioning environments
• Practical understanding of AI/ML fraud model outputs, model performance monitoring, feature evaluation, or data-driven decisioning in an operational environment
At Wave, we value diversity of perspective. Your unique experience enriches our organization. We welcome applicants from all backgrounds. Let’s talk about how you can thrive here!
Wave is committed to providing an inclusive and accessible candidate experience. If you require accommodations during the recruitment process, please let us know by emailing careers@waveapps.com. We will work with you to meet your needs.
We use Google Gemini, a secure AI assistant, during interviews for note-taking purposes only. Notes are kept confidential and are not shared outside the hiring process. This allows our interviewers to stay fully focused on you during the conversation.
This advertised posting is a current vacancy.
Work location
- CA
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About this listing
Published on Lever under the board identifier Waveapps, which is the name the employer’s own job board carries. RoleSprint has not verified the company’s registered or trading name, so it is shown exactly as published rather than tidied up.
RoleSprint is not the employer and not a recruiter. Applications are made on the employer’s own site and never reach us; what RoleSprint does is help you decide whether a role is worth your time and prepare for it if it is.
Published 14 September 2026, last checked 15 days ago. A posting stops being advertised here 90 days after the employer published it, and one the employer takes down is marked closed rather than quietly removed.