Senior Fraud Data Labeling Specialist

Persona•San Francisco, CA

18h

About The Position

Senior Fraud Data Labeling Specialists are experienced contributors who not only perform complex data labeling tasks but also take expanded ownership over quality, process improvement, and cross-functional collaboration. In this role, you will label high-impact datasets from customers, deliver consistently high-quality annotations, and help elevate labeling standards across the team. You will serve as a subject matter expert (SME) for fraud detection workflows, support audits and quality assurance efforts, and partner closely with Engineering, Product, and Operations to refine processes and improve model performance. This role is ideal for someone who is highly detail-oriented, thrives in ambiguous and fast-evolving environments, and demonstrates strong ownership and leadership without direct authority.

Requirements

Proven experience in fraud data labeling, identity verification, or a related domain.
Demonstrated ability to handle complex edge cases and ambiguous scenarios with sound judgment.
Exceptional attention to detail and ability to follow and improve complex instructions.
Strong written and verbal communication skills in English.
Experience conducting audits or quality assurance reviews.
Ability to work independently while driving alignment across teams.
Strong sense of ownership, urgency, and accountability.
Comfort operating in a fast-paced environment with changing priorities.
A proactive mindset with a focus on continuous improvement.

Responsibilities

Perform advanced image and photo quality assessments.
Conduct ID assessment, verification, and visual field extraction across complex and edge-case scenarios.
Evaluate fraud signals based on evolving detection guidelines and nuanced instructions.
Label machine-learning datasets with a high degree of accuracy and consistency.
Own end-to-end quality for assigned workflows, ensuring adherence to SLAs and quality benchmarks.
Conduct post-process audits, identify trends in labeling errors, and produce actionable error reports.
Mentor and support labelers through guidance, feedback, and best practice sharing.
Identify process gaps and proactively recommend improvements to labeling instructions, tooling, and workflows.
Partner cross-functionally with Engineering, Product, and Operations to provide feedback that improves model performance and operational efficiency.
Contribute to documentation, training materials, and evolving fraud detection playbooks.