About OpenTrain

OpenTrain is the #1 platform for building careers in AI training and data labeling — where people start and grow work teaching AI. We connect contributors with projects that shape how state-of-the-art models behave while offering flexible, remote, skills-first opportunities.

Why AI training work matters

AI systems learn from examples prepared and reviewed by people. Work in AI training includes writing and verifying content, evaluating model outputs, and preparing datasets that make models more accurate, reliable, and useful.

This role sits at the intersection of statistics, reproducible research, and dataset creation: your problems and solutions will directly inform model training, evaluation, and fine-tuning.

• Fully remote, flexible work that fits around other commitments.
• Cutting-edge contributions: your outputs influence how AI handles statistical reasoning and problem-solving.

The role

You will design original, research-style computational statistics problems that require complex reasoning and cannot be solved manually within a reasonable timeframe. For each problem you will verify correctness using Python and standard scientific libraries and deliver clear, fully reproducible solutions.

This is a contractor, part-time assignment targeting 20+ hours per week. Work products are text-based problem statements and reproducible code and documentation intended for text-generation, evaluation, and fine-tuning workflows.

• Create problems reflecting real-world workflows: estimation, inference, simulation, optimization, and related research tasks.
• Verify solutions using Python and libraries such as NumPy, SciPy, Pandas, SymPy (or equivalents).
• Document problem statements and fully reproducible correct solutions (code + explanation).
• Label types supported by this role: TEXT_GENERATION, EVALUATION_RATING, FINE_TUNING.

What you'll do day to day

• Design novel computational-statistics problems that demand algorithmic or numerical approaches rather than manual calculation.
• Implement and run Python verification scripts or notebooks using standard scientific libraries to confirm solutions.
• Provide clear, reproducible documentation and step-by-step solutions that others can run and validate.
• Follow detailed annotation/guideline documents and incorporate QA feedback to refine problems and solutions.
• Collaborate asynchronously with project reviewers and respond to requests for clarifications or corrections.

Requirements

You must supply an English CV that includes contact details (email and phone) and a statement of your English proficiency level.

• Bachelor’s degree or higher in Statistics or a closely related field (required).
• Minimum 2 years of professional statistics experience.
• Advanced Python skills for verification and analysis; experience with scientific libraries (NumPy, SciPy, Pandas, SymPy) is required.
• Hands-on text annotation or review experience and familiarity with research-style computational statistics problems.
• Ability to follow detailed annotation guidelines, accept QA feedback, and produce corrected deliverables.

Location & acquisition restrictions

OpenTrain cannot acquire contributors from the following locations: Iran, Cuba, North Korea, Syria, Sudan, Venezuela, Myanmar, Russia, Belarus, Palestine, Switzerland, China, Taiwan, Kenya, and the listed U.S. states and several territories and dependencies. Full restricted list provided in the original project details.

• Restricted list includes many countries, territories, and U.S. states; check project acquisition rules before applying.

Compensation, schedule, and employment type

This is a contractor, part-time role with expected commitment of 20+ hours per week. Pay is in USD and ranges up to $60 per hour; the role indicates an hourly range of $15–$60 and an hourlyRate value of $60 in project details.

• Employment types: CONTRACTOR, PART_TIME.
• Time requirement: 20+ hours/week.
• Pay: up to $60/hour USD (hourly range indicated as $15–$60).

Who should apply

• Statisticians, quantitative researchers, and data scientists who enjoy crafting reproducible problems and code.
• Candidates with prior annotation or review experience who can follow guidelines and iterate from QA feedback.
• Intermediate-level professionals with solid Python and scientific-library experience and a research-oriented mindset.

How to apply

Submit your application through OpenTrain with an English CV that includes your email, phone number, and a note on English proficiency. Applications that meet the stated requirements will be considered for onboarding to this contractor project.

• Required: English CV with contact details and English proficiency level.
• Be prepared to demonstrate reproducible code and clear problem write-ups if requested during selection.