AI Data Quality Analyst & DB Reviewer (Remote)

prolificacademicltd; United States; Remote; USD 21,000 - 34,000; Full time

Job summary

Prolificacademicltd is seeking individuals to contribute database and records expertise to the training and evaluation of AI systems through paid, project-based tasks. Jobs can be completed remotely with flexible scheduling.

After a brief skills assessment, successful applicants may review AI outputs, identify data quality issues, and provide examples to help evaluate how systems handle real-world data across diverse formats.

Qualifications

• Professional experience in high-volume data entry, data processing, database administration, or records management.
• Strong attention to detail when checking large datasets for duplicates, inconsistencies, and formatting problems.
• Ability to process structured and unstructured information efficiently while maintaining accuracy.
• Understanding of data formats and validation rules, with the judgment to spot logical or factual errors in generated outputs.
• Written English skills sufficient to assess clarity and correctness.

Responsibilities

• Check AI-generated records and data extractions for accuracy, completeness, and consistent formatting.
• Review datasets for categorization, labeling, and field-mapping errors, documenting issues against quality criteria.
• Create sample data-entry cases, including ambiguous or conflicting inputs, to evaluate model performance.
• Annotate and classify examples so systems can learn data structures and validation rules.
• Compare generated results with established data-entry standards and flag inconsistencies.

Skills

High-volume data entry

Data processing

Attention to detail

Quality assurance

English writing

Tools

Excel

Google Sheets

Airtable

SQL

Access

Job description

Prolificacademicltd is seeking individuals to contribute database and records expertise to the training and evaluation of AI systems through paid, project-based tasks. Jobs can be completed remotely with flexible scheduling.

After a brief skills assessment, successful applicants may review AI outputs, identify data quality issues, and provide examples to help evaluate how systems handle real-world data across diverse formats.