Remote Data QA & Labeling Specialist

prolificacademicltd; United States; Remote; USD 28,000 - 40,000; Full time

Job summary

Prolific Academic Ltd is seeking a detail-oriented data quality reviewer for paid, task-based work involving AI-generated records. You will review entries, label data, and assess how systems handle structured and messy inputs in a fully remote setup.

After a short skills assessment, successful applicants may review generated records, create realistic examples, and identify labeling errors to ensure data quality and consistency across datasets.

Qualifications

• Experience in high-volume data entry, data processing, database administration, or records management.
• Attention to detail when finding duplicates, inconsistencies, and formatting problems.
• Ability to process structured and unstructured information efficiently without sacrificing accuracy.
• Familiarity with data formats and validation rules; ability to spot factual/logical errors.
• Written English sufficient to assess clarity and correctness; a payment account is required.

Responsibilities

• Check AI-generated entries and extracted records for accuracy, completeness, and consistent formatting.
• Create realistic examples involving ambiguous fields, conflicting information, or imperfect source data.
• Identify categorization, labeling, and field-mapping errors in generated datasets.
• Classify samples and compare outputs with data quality rules and professional standards.

Skills

Data entry

Data processing

Records management

Attention to detail

Structured data processing

Validation rules

English writing

Tools

Excel

Google Sheets

Airtable

SQL

Access

CRM

Document management systems

Job description

Prolific Academic Ltd is seeking a detail-oriented data quality reviewer for paid, task-based work involving AI-generated records. You will review entries, label data, and assess how systems handle structured and messy inputs in a fully remote setup.

After a short skills assessment, successful applicants may review generated records, create realistic examples, and identify labeling errors to ensure data quality and consistency across datasets.