Data Science (AI Trainer)

MissionHires; United States; Remote; USD 55,000 - 172,000; Part time

Job summary

MissionHires is seeking a detail-oriented Data Scientist contractor to design and evaluate AI-driven data-science tasks. You will create realistic workflows, prompts, datasets, and rubrics, then assess frontier AI outputs against professional standards.

Applicants should have 2+ years of applied data science experience, strong Python/SQL skills, and the ability to work independently in a fully remote, contract role. Flexible scheduling with minimum 10 hours per week is offered.

Qualifications

• 2+ years of professional applied data science experience.
• Proficiency in Python and SQL.
• Ability to own multi-step analyses end-to-end.
• Excellent written and verbal English.
• Experience evaluating AI outputs and creating rubrics.

Responsibilities

• Design challenging, realistic data science tasks based on professional workflows.
• Create or anonymize realistic professional files needed to complete each task.
• Run tasks through frontier AI models and evaluate analyses, code, models, and deliverables.
• Compare model outputs from identical prompts and document strengths/weaknesses.
• Write detailed grading rubrics and tests for data-quality and interpretation.
• Identify concrete failures like data leakage, confounding, and incorrect joins.
• Contribute expertise across analytics, ML, experimentation, and data-engineering-adjacent work.
• Review and refine tasks developed by other experts.
• Propose, build, and run complete task scenarios with feedback revisions.

Skills

Data science experience

Python/SQL

English fluency

Independent contributor

Attention to detail

Education

Bachelor's in quantitative field

Advanced degree preferred

Tools

Claude

ChatGPT

Job description

When AI gets the details wrong in your field, real people pay for it. The people who know the work best have to be the ones training these models. You’re not being replaced by AI. You’re making sure it’s used accurately and safely.

How You'll Contribute

• Design challenging, realistic data science tasks based on professional workflows, including scenarios, colleague-style prompts, and supporting materials such as datasets, schemas, notebooks, metric definitions, and stakeholder briefs.
• Create or anonymize realistic professional files needed to complete each task.
• Run tasks through frontier AI models and evaluate the resulting analyses, code, models, SQL queries, and written deliverables against professional standards.
• Compare model outputs produced from identical prompts and files, determine which is stronger, and document where each response falls short.
• Write detailed grading rubrics and tests defining what a correct deliverable must include, such as appropriate data-quality handling, statistical tests, validation methods, and interpretations.
• Identify and document concrete failures, including data leakage, confounding, incorrect joins, misapplied statistical tests, fabricated or ignored data, code that does not meet the brief, and unsupported interpretations.
• Contribute expertise across analytics, machine learning, experimentation, forecasting, and data-engineering-adjacent work.
• Review and refine tasks developed by other subject-matter experts.
• Propose, build, and run complete task scenarios based on real professional experience, incorporating expert-review feedback and revisions.

What Makes You a Great Fit

• 2+ years of professional applied data science experience preferred in analytics, machine learning, statistical modeling, experimentation, analysis-adjacent data engineering, or research; candidates with less experience may be considered if their practical work is substantial.
• Bachelor’s degree in progress or completed in a quantitative field; advanced degrees are a plus but not required.
• Working proficiency in Python and/or SQL, including the ability to write, debug, read, and explain analysis code.
• Depth in at least one area: product or business analytics and BI, applied machine learning, statistics and experimentation, forecasting, analysis-adjacent data engineering, or applied NLP/computer vision with shipped work.
• Experience independently owning a multi-step analysis from raw or messy data through cleaning, modeling, interpretation, and decision-making.
• Ability to evaluate whether results are valid and explain issues such as leakage, confounding, selection effects, multiple testing, power, and business or scientific interpretation.
• Current or recent hands-on individual-contributor experience; candidates whose experience is solely managerial are not a fit.
• Ability to draw on real-world workflows and professional experience to create scenarios that test whether AI can perform the work correctly.
• Full professional or native-level written and spoken English, with the ability to clearly explain complex reasoning and why an output is incorrect.
• Strong attention to detail, comfort with ambiguity, and the ability to verify claims against underlying data, sources, or facts.
• Ability to independently interpret feedback, determine what is correct, and apply it without extensive direction.
• Ability to ramp quickly on unfamiliar work using written instructions and incomplete source material.
• General familiarity with AI and LLM tools such as Claude or ChatGPT and the judgment to distinguish sound reasoning from plausible but incorrect answers.
• Availability for at least 10 hours per week, with consistent availability preferred.
• Based in the United States, Canada, or the United Kingdom.
• Applicants must complete an unpaid skills assessment lasting approximately 2 to 4 hours; all assessment work must be completed independently without AI tools.
• Pay of $40 to $125 USD per hour, paid via PayPal on a regular cadence.
• Fully remote contract work with flexible scheduling.
• Minimum commitment of 10 hours per week with no weekly maximum; full-time hours may be available.
• Multi-day task timers that allow work to be distributed across several days.
• Access to Claude and ChatGPT through the project; no personal subscription is required.
• Regular paid projects after successful onboarding and acceptance.
• Short paid training project covering task, file, and rubric development.
• Written feedback, onboarding materials, dedicated Slack support, and office hours.