About The Position

In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.

Requirements

  • Bachelor's degree or equivalent professional experience in Data Science, Statistics, Applied Mathematics, Computer Science, Operations Research, or a related quantitative field.
  • Minimum of two years’ professional, academic, or research background engaging with statistical models, data visualizations, or analytical reporting.
  • Advanced proficiency in interpreting axes, legends, units, scales, confidence intervals, and statistical annotations across diverse chart types.
  • Demonstrated ability to clearly articulate analytical processes and quantitative reasoning in high-quality written and verbal English.

Nice To Haves

  • Bilingual proficiency in English and, ideally, a second language relevant to your domain.
  • Experience with technical documentation, publication, assessment design, or structured analytical task development.
  • Background in producing data-annotation tasks, benchmark questions, technical reports, or related materials for data science, statistics, or AI-driven projects.

Responsibilities

  • Interpret complex data science and statistical visualizations—such as Sankey diagrams, heatmaps, calibration curves, residual plots, correlation matrices, and distribution plots—applying advanced professional judgement and terminology.
  • Design clear, objective, multi-step analytical tasks and questions based on challenging visualizations, emphasizing quantitative reasoning beyond surface-level chart reading.
  • Deliver precise, unambiguous answers with thorough, step-by-step written explanations, including explicit calculations, statistical inferences, or modeling logic as required.
  • Utilize exact data science and statistical language, ensuring accuracy in referencing units, scales, axes, legends, and annotations in all outputs.
  • Create tasks that require graphical reasoning, trend analysis, comparison, interpolation, and statistical inference, always avoiding ambiguous or subjective prompts.
  • Participate in remote collaboration, contributing via written and verbal channels, incorporating feedback, and maintaining consistent communication with project stakeholders.
  • Consistently produce objectively verifiable tasks and solutions to support the rigorous training and evaluation of advanced AI models.
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