AI Data Quality Analyst - Remote & Flexible Hours

Prolific; Jal (NM); On-site; USD 28,000 - 40,000; Full time

Job description

Database Administrator - Graduates - AI Training

About Prolific

Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world. Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.

Compensation & Work Hours

Researchers pay up to $25 per hour for tasks that require approximately one hour of uninterrupted work, with many tasks shorter. Flexible hours and ability to work from home.

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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