Remote Spanish Data Annotator for Scam and Fraud Detection

Alpha CRC Ltd.; México; Remote; MXN 202,000 - 405,000; Part time

Benefits offered by this job

Fully remote freelance engagement

International team collaboration

Flexible remote working environment

Job summary

Alpha CRC Ltd. is seeking detail-oriented native Spanish speakers to support the development of AI models for scam and phishing detection. You will review SMS and email messages, apply established guidelines, and contribute to high-quality training data for machine learning systems.

This is a fully remote, freelance opportunity. Ideal candidates have native Spanish and excellent English, strong reading comprehension, and comfort with large volumes of repetitive data while maintaining accuracy.

Qualifications

• Native-level Spanish required with excellent English.
• Ability to follow detailed annotation guidelines.
• Experience labeling data and using spreadsheets.
• Comfort with large volumes of repetitive data and accuracy.
• Strong communication and remote collaboration skills.

Responsibilities

• Review SMS and email messages and classify according to guidelines.
• Apply labels consistently and maintain high quality.
• Process up to ~2,500 messages per day with accuracy.
• Escalate ambiguous cases and participate in calibration sessions.
• Follow project documentation and communicate questions to the team.

Skills

Spanish native

English proficiency

Attention to detail

Data annotation

Spreadsheet proficiency

Remote collaboration

Time zone flexibility

Independent work

Strong communication

Tools

Spreadsheet software

Job description

Alpha CRC Ltd. is seeking detail-oriented native Spanish speakers to support the development of AI models for scam and phishing detection. You will review SMS and email messages, apply established guidelines, and contribute to high-quality training data for machine learning systems.

This is a fully remote, freelance opportunity. Ideal candidates have native Spanish and excellent English, strong reading comprehension, and comfort with large volumes of repetitive data while maintaining accuracy.