Fraud and Abuse Operations Analyst

Safety and Security

Safeguard Plaid's Platform: Participate in the abuse on-call rotation, directly protecting our users and customers by responding to and resolving fraud and abuse events. Your timely actions will be instrumental in maintaining trust and security.

Drive Investigations and Mitigate Risks: Investigate fraud and abuse claims from diverse sources, partnering with senior teammates on complex cases. Your findings will inform decisions and strategies, directly impacting Plaid's ability to prevent future incidents and minimize financial losses.

Support Incident Response: Help triage and manage fraud and abuse events as internal incidents (SEVs), collaborating across teams to drive resolution. Your contributions in these situations will minimize disruption and protect Plaid's reputation.

Inform Product Strategy: Partner with Consumer Risk and other product teams, providing operational insights from on-call and investigations. Your contributions will help inform the development of effective fraud mitigation solutions and strengthen Plaid's defenses.

Qualifications

• 3+ years of experience in fraud/abuse operations, trust & safety, and/or incident response
• Incident response and investigations: Experience triaging fraud or related incidents, with tight containment deadlines.
• Experience managing escalations and making informed trade-off decisions with multiple stakeholders.
• Ability to coordinate and communicate effectively with internal Legal, Compliance, Comms and other risk management teams during incident triage.
• A battle-tested mental model for balancing fraud mitigation vs. conversion, and the ability to calibrate and apply this mental model in the context of Plaid's business objectives under pressure.
• Fraud and abuse detection and prevention context: Working knowledge of fraud typologies–account takeover, different types of payments fraud, ACH fraud, chargebacks, kiting, rewards harvesting, etc.
• Fraud and abuse intelligence: Dark and grey-web navigation and investigation experience, ability to assess source credibility, and translate external intelligence into actionable insights.
• Experience translating fraud investigations and research into actionable detection signals, rules, and labeled datasets, while partnering closely with DS/ML and product teams to improve feature sets, labeling quality, evaluation frameworks, and model decay monitoring.
• Experience developing and maintaining threat models for authentication and other high-risk product surfaces, with a track record of proactively identifying emerging risks.
• Data analysis skills: Proficiency with SQL for querying data sets and building reports. Comfortable with common analysis tools and manipulation tools. Ability to spot anomalies and fraudulent patterns in logs and other data sets (transactions, request data, etc.), with judgment to escalate ambiguous cases.
• Operational depth and maturity: Experience contributing improvements to operational processes and controls.
• Strong written and verbal communication skills.

Nice-to-Haves

• Familiarity with machine learning models for fraud detection.
• Fraud domain certifications (e.g., CFE/fraud examination background).
• Exposure to production ML model lifecycles and metrics for drift/decay.
• Effective use of AI and AI agents to accelerate investigations and research.