Data Entry Clerk – Graduates – AI Training – Torreón, Mexico

Data Entry Clerk – Graduates – AI Training

The role

We’re looking for Data Entry Specialists to join our Expert Network to help train and evaluate cutting‑edge AI models using real data expertise. If you have the necessary experience, we’ll send you a quick 10‑ to 15‑minute test to assess your skills and suitability for AI tasks. If successful, you’ll be invited to join Prolific as a participant, where you’ll get paid to train and evaluate powerful AI models.

Researchers looking for your skills tend to pay up to $25 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter.

What you’ll bring

• Professional Experience: years of experience in high‑volume data entry, data processing, database management, or records administration.
• Accuracy & Attention to Detail: a proven track record of maintaining high accuracy rates across large datasets, with a sharp eye for inconsistencies, duplicates, and formatting errors.
• Speed & Efficiency: high typing speed and the ability to process structured and unstructured data quickly without sacrificing quality.
• Data Literacy: familiarity with data formats, validation rules, and the ability to identify when AI‑generated outputs contain logical or factual errors.
• Communication Skills: solid written English skills sufficient to assess clarity and correctness in AI‑generated text.
• Language Proficiency: multilingual capabilities are a significant plus, especially for evaluating data quality across localized datasets.
• A PayPal account to receive payment from our clients.

What you’ll be doing in the role

• Evaluate AI Data Outputs: review AI‑generated data entries, extractions, and structured records for accuracy, completeness, and formatting consistency.
• Simulate Data Entry Tasks: create realistic data entry scenarios and edge cases to test how AI handles messy inputs, ambiguous fields, or conflicting records.
• Audit AI‑Generated Datasets: review AI‑produced data for errors in categorisation, labelling, or field mapping, and flag issues against standard data quality rubrics.
• Annotation & Labelling: tag and classify data samples to help AI models learn correct data structures, formats, and validation rules.
• Quality Assurance: compare AI outputs against established data entry standards to ensure they meet professional accuracy and consistency benchmarks.

Key Technologies

• Data Tools: proficiency with Microsoft Excel, Google Sheets, or database platforms such as Airtable, SQL, or Access.
• Data Management Systems: experience with CRM platforms, ERP systems, or document management tools.
• Documentation: familiarity with Confluence, Notion, or similar platforms for referencing data standards and internal guidelines.

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