AI Data Science Domain Expert

What you would do

• Edit and refine AI-generated analytical outputs for clarity, correctness, and domain appropriateness
• Develop and optimize prompts that steer AI models toward analytically sound results
• Apply rubric-based frameworks to assess model performance and identify improvement areas
• Conduct independent research to validate facts and enhance data quality assurance
• Create clear technical summaries and reports from complex analytical findings

Who they want

• 3+ years of hands-on work in analytics, statistical modeling, AI systems, or quantitative research
• Proven track record producing or reviewing research papers, analytical reports, or technical documentation
• Advanced written communication skills and facility with professional, technical writing styles
• Background in dataset labeling, text assessment, or structured evaluation frameworks preferred
• Master's, PhD, or advanced degree; background from selective universities or research organizations encouraged

Main skills

What the interview asks about

1. 1.Prompt engineering for analytical accuracy

   Effective prompt design directly influences whether AI models produce statistically sound or flawed reasoning, making this a critical skill for training data quality.

   For example: “You're tasked with designing a prompt to train an AI model to perform hypothesis testing on a dataset with skewed distributions. Walk me through how you'd structure that prompt to ensure the model respects the distributional assumptions.”

2. 2.Rubric-based model evaluation

   Applying consistent evaluation criteria prevents subjective judgment drift and ensures training signals are aligned with analytical standards, which is essential for model convergence.

   For example: “You receive 10 model outputs attempting to forecast quarterly revenue under three economic scenarios. What criteria would you use in your rubric to score whether each forecast properly accounts for scenario-specific risks?”

3. 3.Fact-checking and data validation

   Errors in training data propagate through the entire model, so catching inaccurate claims or unsupported inferences before annotation is critical to model reliability.

   For example: “A model proposes using a specific statistical test for comparing two sample distributions, but you suspect the test assumes normality. How would you verify your concern and what would you document in feedback?”

4. 4.Technical writing for complex concepts

   Clear explanation of analytical reasoning helps other domain experts verify your judgments and ensures training feedback is actionable rather than ambiguous.

   For example: “Summarize a complex statistical result involving interaction effects and heteroscedasticity so that a non-statistical audience understands both the finding and its limitations in under 200 words.”

5. 5.Identifying model reasoning gaps

   Spotting where model outputs fail to address edge cases or overlook domain-specific constraints reveals exactly where training data improvements are needed.

   For example: “A model generates a financial forecast but ignores regulatory changes scheduled for next quarter. How would you structure written feedback that helps the training team understand what domain knowledge the model is missing?”

A task you may get

Design a rubric to evaluate whether an AI model correctly interprets a research paper's methodology section, then score two model outputs against that rubric with written justification for each score.

How to prepare

• Review 2-3 recent papers in your field and identify the key analytical assumptions and methodological decisions that a model must understand
• Draft 3-4 example prompts that elicit detailed reasoning from language models, then refine them based on actual model outputs you generate
• Collect examples of technical writing (reports, memos, summaries) you've authored and reflect on which elements made complex ideas clear
• Practice explaining a subtle analytical error in your field (e.g., a common statistical mistake or assumption violation) in clear, written form

The facts

Pay

$100–200/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

Expert

Posted

8/4/2026

Places left

15

We wrote this page from the public micro1 listing. It may be out of date, so read the full posting before you apply.