Trust and Safety Specialist
WelocalizeCodingNew
- Where
- Mexico
- Language
- English
- Type
- Full-time, remote
- Field
- Coding
- Posted on Welocalize
- First seen here
- Checked, still open
From the Welocalize listing
About the Team
Welo Data's Trust & Safety function protects the integrity of a global, distributed workforce that powers AI data and annotation work. We run a multi-layered fraud detection stack combining identity verification checks, in-house behavioral signals, and platform activity data, and are actively building out a Trust & Safety Committee spanning four core workstreams: identity verification, location verification, account integrity, and multi-accounting detection. This is a hands-on investigative role, not a policy-only seat, you'll be working live cases from day one.
About the Role
We're looking for a Trust & Safety Specialist, based in Mexico, to join our investigations team, working active fraud cases and supporting the broader fraud strategy program. You'll come in with real T&S, fraud/risk, or identity-verification experience from another platform, marketplace, gaming company, fintech, IDV vendor, or crowdsourced-workforce environment, and be ready to run investigations, process removals, and spot fraud patterns from week one.
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.
Text above is the platform's own listing, shown as published.
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Terms in this listing
- Annotation: Adding labels or notes to data such as text, images, audio or video so a model can learn from it.
- Edge case: An unusual item the guidelines do not clearly cover. Projects usually ask you to flag it rather than guess.
- RLHF: A way of improving a model by having people compare or score its answers, training a reward model on those judgements, then tuning the model to produce answers people prefer.
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