Data Analyst - Remote

YO AI Labs • Artificial Intelligence • LLM & Generative AI • 18 days ago
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Salary
$62k – $120k per year
Location
In office (Austin)

Confirmed on the employer's own hiring board on Oct 14, 2026. First seen by Alion on Sep 27, 2026. YO AI Labs scores A on the Alion truth index.

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YO AI Labs

YO AI Labs is an artificial intelligence platform specializing in AI model training, evaluation, and data annotation services. Headquartered in San Francisco, California, United States, the company connects domain experts and remote specialists across the globe to review, test, and benchmark generative AI model outputs. Through its decentralized workforce solutions and structured evaluation workflows, it enables frontier technology developers to enhance the accuracy, safety, and reliability of next-generation artificial intelligence systems.
yoailabs.com • HQ: San Francisco, United States • AI Evaluation & Observability • AI Training Data & Annotation • 5000+ employees
Tech stack of YO AI Labs

Data Analyst

Role Type: Contractor

Location: Remote

micro1 is seeking Data Analysts to support a customer project focused on business data analysis, document review, privacy, and quality assurance for AI training. No prior AI experience is required.

Key Responsibilities

  • Review and analyze business datasets for accuracy and completeness.
  • Conduct document reviews while following PII and data privacy requirements.
  • Identify and resolve data quality issues, inconsistencies, and anomalies.
  • Provide clear findings and feedback to support AI training.
  • Support data quality and QA standards.
  • Maintain compliance with privacy and security guidelines.

Required Skills

  • PII & Data Privacy
  • Document Review
  • Data Analysis
  • Data Quality Assurance
  • Attention to Detail
  • Analytical & Problem-Solving Skills
  • Written & Verbal Communication

Qualifications

  • Experience analyzing large-scale business datasets.
  • Knowledge of PII handling, data privacy, and security practices.
  • Background in data review, QA, auditing, or related analytical work.
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