Trust and Safety Specialist

About Welo Data

Welo Data, a Welo Global brand, is the multilingual data and evaluation partner for foundation labs and enterprises deploying GenAI systems globally. They deliver the human judgment, data infrastructure, and evaluation systems that ensure AI models perform reliably across languages, cultures, and real-world contexts, at every stage from training through deployment.  Its global network of 500,000+ vetted experts spans 300+ languages and locales, enabling high-quality multilingual data creation and structured model evaluation across the full spectrum of modern AI applications — from large language models and voice and speech systems to agentic workflows and robotics and embodied AI. This breadth of linguistic, cultural, and domain expertise enables Welo Data to address critical AI development challenges, including safety, bias, inclusivity, and cross-lingual reliability. A unified global operating model, led by specialized program and quality experts and grounded in assessment-driven talent selection, localized rubrics, and continuous calibration, ensures consistent performance across languages, domains, and modalities. Underpinning all of this is NIMO™ (Network Identity Management and Operations), Welo Data's proprietary identity and fraud-prevention framework. Built to maintain data integrity and workforce trust across a global contributor base, NIMO combines advanced verification, continuous monitoring, and structured QA to ensure every dataset is accurate, traceable, and culturally grounded. welodata.ai   

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)

4+ 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.