Fraud Researcher

Plaid•San Francisco, CA

About The Position

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.

Requirements

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)

Nice To Haves

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

Responsibilities

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

Benefits

Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at [email protected].

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