Data Science-AI Trainer

Data Science AI Trainer Overview

Apply below after reading through all the details and supporting information regarding this job opportunity.

• The Data Science AI Trainer role involves evaluating AI models on real-world data science tasks.
• Contribute to improving AI systems by designing realistic, challenging tasks based on professional experience.
• Work remotely with flexible scheduling, engaging in project-based AI training activities.
• Collaborate with frontier AI labs to enhance human-aligned AI capabilities.
• Utilize your expertise to assess AI outputs against professional standards.
• Gain access to advanced AI tools like Claude and ChatGPT for project work.
• Participate in structured, self-directed tasks requiring clear written reasoning and technical depth.
• Engage in an innovative role that combines data science expertise with AI evaluation.
• Contribute to the development of trustworthy AI systems through professional collaboration.

Data Science AI Trainer Key Responsibilities & Duties

• Design and execute realistic data science tasks based on professional workflows.
• Evaluate AI model outputs against professional standards and document findings.
• Develop grading rubrics and tests to assess deliverables for accuracy and completeness.
• Identify and flag concrete failures in AI outputs with evidence and reasoning.
• Contribute to analytics, machine learning, and experimentation tasks.
• Review and refine tasks created by other experts to ensure quality and relevance.
• Craft scenarios that test AI systems' ability to perform professional-level work.
• Provide detailed feedback and revisions to improve AI task outcomes.
• Collaborate with a team of professionals to advance AI training methodologies.

Data Science AI Trainer Job Requirements

• Bachelor’s degree in a quantitative field, with practical applied work experience.
• Minimum of 2 years professional experience in applied data science or related fields.
• Proficiency in Python and SQL for writing and debugging analysis code.
• Depth in analytics, machine learning, experimentation, or data engineering.
• Ability to independently own multi-step analyses from raw data to actionable insights.
• Strong written communication skills to articulate reasoning and evaluate results.
• Comfort with ambiguity and attention to detail in professional tasks.
• Familiarity with AI tools and judgment to assess their outputs critically. xhqgsiq
• Availability for at least 10 hours per week in a remote setting.