Trust and Safety Specialist

What You Will Do

Run investigations end-to-end: triage flagged accounts, review evidence (identity signals, behavioral flags, platform activity data), reach a disposition, and process removals in line with policy and SLA.

Own IDV review: verify identity and location signals for flagged or high-risk accounts, distinguishing genuine edge cases from fraud. 

Conduct manual audits and reviews: prioritizing cases by risk and signal strength rather than intake order. 

Detect multi-accounting and account integrity abuse: recognize patterns across linked accounts, device/location overlap, and behavioral signals. 

Apply a hypothesis-driven investigation method: form a hypothesis, test it against the evidence, and reach a disposition that can be replicated and audited. 

Contribute to detection improvement: escalate emerging fraud vectors, false-positive/false-negative patterns, and rule gaps to the fraud strategy team. 

Handle sensitive data responsibly: apply GDPR-aligned data handling and deletion practices in every case. 

Collaborate cross-functionally: partner with Research Labs, Quality Strategy Architecture (QSA), and Forward Deployment Engineering on tooling gaps and process fixes surfaced by your casework

 

What You Bring (Must Have-s)

5+ years in a T&S, fraud/risk, or identity-verification investigations role — from a platform/marketplace T&S team, a fintech/payments risk team, an identity-verification vendor, or a crowdsourced/gig-workforce trust function.

Direct experience with identity/fraud signals — device fingerprinting, IP/geolocation checks, document verification, or behavioral fraud scoring.

Multi-accounting / account integrity pattern recognition — you've dealt with linked-account abuse, farming, or account sharing before, not just single-account review.

Case documentation discipline — you write clean, evidence-backed case notes that would survive an audit or an appeal.

Comfort with ambiguity and volume — you can prioritize a queue by risk, not just clear it by count.

Working knowledge of data privacy obligations (GDPR or equivalent) as they apply to case handling and data retention/deletion.

Nice to Have

Experience specifically in AI/ML data annotation, RLHF, or research operations workforce platforms.

Exposure to rule-based fraud detection engines and an opinion on rule sprawl vs. signal quality. 

Familiarity with clustering/anomaly-detection approaches to fraud (even at a conceptual level).

Case management system experience (any ticketing or knowledge-base tooling). 

Recommended Education

Bachelor's degree in Criminology, Law, Criminal Justice, Cybersecurity, Information Security, Data/Computer Science, or a related field.

Equivalent practical experience in T&S, fraud investigations, or identity verification will be considered in place of a degree.

Relevant certifications are a plus: CFE (Certified Fraud Examiner), CAMS (Certified Anti-Money Laundering Specialist), or IDV/identity risk vendor certifications.