updated 1 month ago
AI Data Science Domain Expert
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Job description
Text:
TL;DR
AI Data Science Domain Expert (AI Evaluation/Data Science): Reviewing, refining, and evaluating AI-generated technical and analytical content with an accent on data science accuracy, prompt engineering, and structured quality assessment. Focus on annotating data, fact-checking complex technical information, and providing feedback to improve AI model reasoning and performance.
Location: Remote
Company
delivers consulting services and projects focused on technology and AI systems.
What you will do
- Review, edit, and refine AI-generated technical and analytical content for accuracy, clarity, and relevance.
- Develop, optimize, and evaluate prompts to improve AI model performance.
- Assess AI outputs using rubrics and provide structured feedback.
- Conduct independent research and fact-checking to validate technical information.
- Annotate data, support AI-training quality assurance, and interpret complex datasets.
- Prepare technical reports and summaries while collaborating remotely with project teams.
Requirements
- 3+ years of experience in data science, machine learning, applied AI, statistics, quantitative analytics, or data analytics.
- Strong critical thinking, analytical reasoning, problem-solving, and attention to detail.
- Experience producing or reviewing research papers, analytical reports, technical documentation, experiment summaries, or data-driven recommendations.
- Strong technical and report-writing, communication, content-review, and editing skills.
- Ability to perform data interpretation, fact-checking, independent research, and quality assurance.
- Experience with data annotation or rubric-based evaluation is preferred.
Nice to have
- Familiarity with prompt engineering, AI output evaluation, fact-checking, or RLHF.
- Master's degree, MBA, PhD, or another advanced degree.
Culture & Benefits
- Part-time contractor engagement.
- Remote collaboration with project teams.
- Opportunity to contribute to next-generation AI systems and improve model accuracy, reasoning, and performance.
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