Database Administrator
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Database Administrator - Graduates - AI TrainingAbout ProlificProlific 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.The roleWe're looking for Database Administrator 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
- 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.
- 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.