Data Entry Keyer

What you would do

• Create evaluation tasks that simulate real data entry and validation work in regulated or compliance-sensitive environments
• Build complex datasets across multiple formats (spreadsheets, PDFs, CSVs) containing realistic data quality issues
• Specify the correct outcome for each task, outlining exact error scenarios and resolution steps the AI must handle
• Author comprehensive grading rubrics with 35+ criteria to assess AI agent accuracy and error detection capability
• Collaborate asynchronously to refine evaluation materials and iterate based on feedback

Who they want

• Significant background in data entry, accuracy checking, or data validation in highly regulated sectors such as healthcare, finance, or legal work
• Demonstrated track record achieving measurable accuracy standards with documented error rates and quality outcomes
• Strong capability detecting and analyzing data inconsistencies, truncations, and formatting irregularities across file types
• Proficient written English for drafting complex evaluation tasks and comprehensive grading standards
• Exceptional attention to detail, disciplined process-oriented work style, and comfort in remote, asynchronous environments

Main skills

What the interview asks about

1. 1.Realistic error design

   AI training quality depends on error types reflecting actual operational challenges; your ability to embed authentic data problems shows deep domain knowledge.

   For example: “In healthcare claims data, describe three realistic data errors you've encountered (not simple typos) that would appear in a CSV and explain why each matters for AI training.”

2. 2.Multi-format dataset creation

   Real data validation involves multiple formats with different error patterns; you need technical skill embedding consistent, traceable errors across formats.

   For example: “Design how you'd create a dataset with a customer master record issue appearing consistently across a spreadsheet, a PDF export, and a CSV. What makes the error realistic in each format?”

3. 3.Rubric criterion specificity

   35+ criteria require precision; vague rubrics produce unreliable AI training, while overly specific ones miss nuance - your judgment defines what AI learns to recognize.

   For example: “If grading AI's ability to detect truncated names in records, outline 3-4 specific rubric criteria that would distinguish acceptable from unacceptable error detection.”

4. 4.Domain accuracy standards

   Regulated environments have concrete accuracy thresholds; your prior experience with compliance requirements informs what standards the AI must meet.

   For example: “In your regulated domain, what error tolerance exists for AI-assisted data validation? Walk through how you'd define acceptable accuracy rates for different error types.”

5. 5.Task complexity calibration

   Tasks that are too simple or too hard produce poor training; balancing complexity while maintaining realistic scenarios requires judgment from experienced data professionals.

   For example: “You're designing a task combining three different error types in a single dataset. How would you decide the ratio and difficulty to avoid overwhelming or boring an AI system?”

A task you may get

Create a sample evaluation task with a realistic dataset (at least 50 rows) containing 5-7 authentic data quality issues across multiple error categories. Draft 6-8 grading criteria assessing AI agent output quality.

How to prepare

• Compile 3-5 specific examples of data quality errors from your regulated domain work with business impact details
• Draft a mini-rubric (6-8 criteria) for assessing data validation quality from a project you know well
• Review your prior work for documented accuracy standards or error rate metrics you achieved
• Prepare a complex dataset example you've worked with, noting where hidden data problems appear and why they matter

The facts

Pay

$20–35/hr

Open to

Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia

Field

Business Operations

Role type

Generalist

Posted

9/16/2026

Places left

50

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