Fraud Intelligence Lead

Our Fraud Intelligence team's mission is to turn fraud signals into insight that our EPD teams transform into improvements across Protect, IDV, Signal, and Guaranteed Payments. We believe transaction patterns, device signals, identity linkages, and behavioral data are dramatically underleveraged tools in fraud prevention, and we ground our products in what adversaries are actually doing right now.

As the Fraud Intelligence Lead, you will build and run a small, high-leverage team of Fraud Intelligence Analysts (and eventually a Staff Researcher) responsible for live casework across Protect, IDV, and Payments/ACH. You'll operate as a player-coach, hiring and coaching your team while staying close enough to the work to personally pick up casework and SEV response when needed.

RESPONSIBILITIES

Team Building & People Leadership

- Set the casework quality bar: define what rigorous investigation, triage, and reporting look like for the team

- Coach analysts on investigation technique, pattern synthesis, and translating findings into product/model input

Operating Model & Cross-PA Partnership

- Own coverage allocation across the Protect/IDV and Payments/ACH pods, including flexing assignments as volume shifts

- Manage matrixed staffing and time allocation clearly between the Fraud PA and Payments PA

- Represent the Fraud Intelligence team in product and model roadmap discussions, translating casework patterns into strategic priorities

Reporting & Escalation

- Report team health, casework trends, and emerging risks to the Head of Fraud

- Own escalation paths for SEVs and incidents requiring legal, law enforcement, or regulatory involvement

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

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

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

QUALIFICATIONS

- 5+ 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

PREFERRED:

- 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