Our Fraud team's mission is to help companies detect and prevent fraud using Plaid's financial network data. We believe that transaction patterns, device signals, identity linkages, and behavioral data are dramatically underleveraged tools in fraud prevention. Our products - including Protect and Signal - operate at network scale and depend on real-world investigation and research to stay ahead of adaptive adversaries.

As a Senior Fraud Researcher, you will sit at the intersection of live fraud investigation, applied data science, and product innovation. You will lead complex investigations, translate findings into detection improvements, and collaborate tightly with Data Science, ML, and Product teams to shape the next generation of Plaid's fraud capabilities. This is not a purely operational role - your research directly drives features, model inputs, and product design.

Responsibilities:

Live Fraud Investigation & Reconstruction

• Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces
• Provide support to day-to-day fraud operations including SEVs and alert triage
• Reconstruct attacker sequences and hypothesize actor intent and tooling
• Distill patterns from noisy signals into clear narratives and actionable insights
• Bridge investigation outcomes to product and model improvements

Signal & Tool Utilization at Scale

• Operate across Plaid's fraud tooling - dashboards, alerting systems, network signals, and analytics platforms - to detect and validate anomalies
• Stress-test existing capabilities, identify systemic gaps, and define new detection primitives
• Proactively identify gaps in internal fraud tooling and automation, driving enhancements to improve efficiency and scale

Product & Model Partnership

• Collaborate with Data Science, ML/AI, and Product teams to improve labeling, feature sets, evaluation frameworks, and model decay monitoring
• Surface data quality limitations and systematically formalize missing features
• Translate exploratory research into reusable feature pipelines, model inputs, or rule augmentations
• Participate in product discovery, roadmap planning, and post-launch evaluation to ensure fraud-awareness by design

Deep Applied Fraud Research

• Conduct longitudinal and structural analysis of how fraud types manifest in Plaid network data - entity linkages, temporal patterns, attack rotations, tool chains
• Experiment with network/graph analysis, sequence mining, anomaly detection, and custom heuristics where off-the-shelf approaches fail

Ecosystem Monitoring & Knowledge Leadership

• Continuously survey external fraud trends, adversary techniques, tooling, and emerging threat vectors
• Proactively perform threat modeling of abuse surfaces and initiate research proposals when patterns emerge

Case Studies & Reporting

• Produce clear, evidence-backed technical reports and case studies for product, engineering, operations, legal, and executive stakeholders
• Document investigation workflows, attack classifications, and proof-of-concept detection logic
• Drive post-incident learning by capturing lessons from fraud incidents and feeding them back into defenses

Qualifications:

• 3+ years of applied fraud experience in a high-velocity environment (fintech, consumer payments, banking, SaaS, marketplace risk, or security research)
• Investigator mindset: pattern synthesis, hypothesis testing, and skilled triage between signal and noise
• End-to-end investigation experience reconstructing attacker intent and behavior in multi-step attack sequences across accounts, devices, and identities
• Post-containment incident response experience with a deep emphasis on post-mortems and root cause analysis
• Dark and grey-web navigation and investigation experience; ability to assess source credibility and translate external intelligence into actionable insights
• Strong communication: ability to explain complex, ambiguous behavior to technical and non-technical audiences
• Tool fluency with data environments and investigative toolchains (BI tools, anomaly detection, case trackers)

Preferred

• SQL for deep data querying and exploratory analysis
• Python for scripting, rapid prototyping, and analytical workflows
• Graph/network analysis experience to detect linked behavioral structures or actor networks
• Familiarity with rule engines, signal gating, and large-scale monitoring systems
• Experience applying AI tools and agents to accelerate investigations and research workflows
• Ability to translate fraud research into actionable signals, rules, or labeled datasets that improve model performance

Nice to Have

• Fraud domain certifications (e.g., CFE)
• Prior work on consumer identity, payments, or risk platform development
• Exposure to production ML model lifecycles and metrics for drift/decay
• Experience improving internal fraud tooling, automation, or case management systems

Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!

Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.