Data Science AI Trainer

Key Responsibilities

• Design challenging, realistic data science tasks based on day-to-day professional experience, including scenarios, prompts, datasets, schemas, notebooks, metric definitions, and stakeholder briefs.
• Run analytical, machine learning, experimentation, forecasting, SQL, and data engineering tasks through frontier AI models.
• Evaluate model-generated analyses, code, models, queries, and written deliverables against the standard expected of a qualified professional.
• Compare model outputs produced from identical prompts and files, determine which performed better, and document the strengths and shortcomings of each.
• Write detailed grading rubrics and, where appropriate, tests that define correct data quality handling, statistical testing, validation, and interpretation.
• Identify and evidence concrete failures such as leakage, confounding, incorrect joins, misapplied tests, fabricated or ignored data, and off-brief interpretations.
• Independently own multi-step tasks from raw or messy data through cleaning, modeling, interpretation, and decision-oriented conclusions.
• Contribute across analytics, machine learning, experimentation, and data engineering adjacent work while reviewing and refining tasks created by other experts.
• Interpret reviewer feedback, apply revisions, and explain clearly why a model response passes or fails each evaluation criterion.

Required Qualifications

• Working proficiency in Python and/or SQL, including the ability to write, debug, and explain analysis code.
• Professional or native-level written and spoken English with strong written communication.
• Ability to explain complex professional reasoning clearly and concisely, including why a result is incorrect.
• Hands-on practitioner experience at an individual contributor level rather than experience solely in a managerial capacity.
• Ability to draw on real-world experience and day-to-day workflows to create scenarios that evaluate whether an AI system can perform professional work.
• Comfort with ambiguity, attention to detail, and the ability to verify claims against underlying numbers, sources, or facts.
• Ability to interpret feedback, judge its accuracy, and apply it independently.
• Ability to ramp quickly on unfamiliar work from written instructions, including incomplete materials.
• General familiarity with AI and large language model tools, including professional use of models such as Claude or ChatGPT.
• Baseline technology literacy, including comfort with Google Workspace, browser profiles, desktop applications, Excel, Google Sheets, and everyday file handling.
• Availability for at least 10 hours per week, with no weekly maximum.
• Based in the United States, Canada, or the UK.

Education

• Bachelor’s degree or higher in a quantitative field, completed or in progress.

Benefits and Perks

• Fully remote work with flexible scheduling.
• Project-based contract work with a minimum of approximately 10 hours per week and no weekly maximum.
• Full-time hours are available.
• Pay of $40-$125 per hour USD, paid via PayPal on a regular cadence.
• Multi-day task timers allow contributors to spread work across multiple days.
• Access to Claude and ChatGPT is provided through the project; no personal subscription is required.
• Onboarding materials, platform instructions, a dedicated Slack channel, and office hours provide support.
• Contributors are compensated for each accepted completed step.