Data Science-AI Trainer

• Bachelor’s degree in a quantitative field, with practical applied work experience. \n

• Minimum of 2 years professional experience in applied data science or related fields. \n

• Proficiency in Python and SQL for writing and debugging analysis code. \n

• Depth in analytics, machine learning, experimentation, or data engineering. \n

• Ability to independently own multi-step analyses from raw data to actionable insights. \n

• Strong written communication skills to articulate reasoning and evaluate results. \n

• Comfort with ambiguity and attention to detail in professional tasks. \n

• Familiarity with AI tools and judgment to assess their outputs critically.

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• The Data Science AI Trainer role involves evaluating AI models on real-world data science tasks. \n

• Contribute to improving AI systems by designing realistic, challenging tasks based on professional experience. \n

• Work remotely with flexible scheduling, engaging in project-based AI training activities. \n

• Collaborate with frontier AI labs to enhance human-aligned AI capabilities. \n

• Utilize your expertise to assess AI outputs against professional standards. \n

• Gain access to advanced AI tools like Claude and ChatGPT for project work. \n

• Participate in structured, self-directed tasks requiring clear written reasoning and technical depth. \n

• Engage in an innovative role that combines data science expertise with AI evaluation. \n

• Contribute to the development of trustworthy AI systems through professional collaboration. \n

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Data Science AI Trainer Key Responsibilities & Duties

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• Design and execute realistic data science tasks based on professional workflows. \n

• Evaluate AI model outputs against professional standards and document findings. \n

• Develop grading rubrics and tests to assess deliverables for accuracy and completeness. \n

• Identify and flag concrete failures in AI outputs with evidence and reasoning. \n

• Contribute to analytics, machine learning, and experimentation tasks. \n

• Review and refine tasks created by other experts to ensure quality and relevance. \n

• Craft scenarios that test AI systems’ ability to perform professional-level work. \n

• Provide detailed feedback and revisions to improve AI task outcomes. \n

• Collaborate with a team of professionals to advance AI training methodologies. \n

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