This role pays more than
62%
of similar roles. Most pay
$151,475–$215,241
— the shaded band above.
At the midpoint, this role pays about
$191k
versus about
$183k
for comparable roles.
Based on 240 similar postings.
Employer
About Plaid
Plaid is a financial technology company that builds a data network powering digital finance applications, enabling consumers to securely connect their financial accounts to apps and services. Industry: Financial Technology & Data Infrastructure
Plaid currently has
35 open roles
on FindRole.
Listed pay typically runs
$190,800–$262,800
across 35 roles with salary data.
As the Fraud Intelligence Lead, you will join the Fraud Intelligence team to build and lead a high-leverage group of analysts focused on live casework across Protect, IDV, and Payments/ACH. Operating as a player-coach, you will manage team growth, set investigation standards, and oversee coverage for various pods while personally handling complex fraud cases and SEV responses. You will reconstruct attacker sequences, identify patterns in transaction behavior and device signals, and translate these findings into actionable insights for product and model improvements. The role requires expertise in SQL for data querying, Python for scripting and prototyping, and experience with BI tools and investigation toolchains. You will solve critical fraud prevention problems by monitoring external threat vectors and collaborating with Data Science teams to improve feature sets and model performance within the financial services ecosystem.
Lead and coach a team of Fraud Intelligence Analysts while performing hands-on casework and SEV responses.
Define investigation standards and coach analysts on pattern synthesis and translating findings into product inputs.
Manage coverage allocation across different fraud pods and represent the team in product roadmap discussions.
Lead complex investigations to reconstruct attacker sequences, intent, and tooling across accounts and devices.
Translate raw fraud signals into actionable insights for data science, machine learning, and product teams.
Identify data quality limitations and formalize missing features to improve model performance and rule sets.
Monitor external fraud trends and perform threat modeling of abuse surfaces to initiate research proposals.
Manage escalation paths for incidents requiring legal, law enforcement, or regulatory involvement.
What we're looking for
5+ years of applied fraud experience in a high-velocity environment like fintech, banking, or security research.
Experience reconstructing attacker intent and behavior in multi-step attack sequences across accounts, devices, and identities.
Post-containment incident response experience with an emphasis on post-mortems and root cause analysis.
Dark and grey-web navigation and investigation experience to translate external intelligence into actionable insights.
Ability to communicate complex, ambiguous behaviors to both technical and non-technical audiences.
Proficiency in SQL for deep data querying and Python for scripting and analytical workflows.
Experience with BI tools, anomaly detection, and investigative toolchains.
Graph/network analysis experience, rule engine familiarity, or AI tool application (preferred); fraud certifications or ML lifecycle exposure (nice to have).
SQL
Python
AI
Graph Analysis
Anomaly Detection
Data Science
Rule Engines
BI Tools
Case Management Systems
Threat Modeling
Incident Response
Root Cause Analysis
Data Querying
Scripting
Automation