Fraud and Abuse Operations Analyst
$138,000–$163,200 year
On-site · New York City, New York, United States or San Francisco, California, United States
Job Summary
Fraud and Abuse Operations Analyst responsible for responding to fraud and abuse events, investigating claims, and triaging incidents. Partners with product and engineering to inform fraud mitigation strategies, participates in on-call rotation, and helps inform operational improvements through threat modeling, data analysis, and collaboration with Legal/Compliance. Emphasizes protecting Plaid’s platform, reducing financial losses, and synthesizing investigation findings into actionable signals and rules.
Required Qualifications
- 3+ years of experience in fraud/abuse operations, trust & safety, and/or incident response
- 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
- Working knowledge of fraud typologies–account takeover, different types of payments fraud, ACH fraud, chargebacks, kiting, rewards harvesting, etc.
- Familiarity with the tactics, techniques, and procedures (TTPs) used by bad actors to commit different types of fraud on the web
- Familiarity with common mitigation strategies for the same, with a developing point of view on their relative pros and cons
- 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
- 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
- Experience contributing improvements to operational processes and controls
- Nice-to-Haves: Familiarity with machine learning models for fraud detection, Fraud domain certifications (e.g., CFE), Exposure to production ML model lifecycles and metrics for drift/decay
- Effective use of AI and AI agents to accelerate investigations and research
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