Fraud Researcher

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

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

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

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

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

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

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)

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

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