Finance Workflow Realism Reviewer

About OpenTrain AI

OpenTrain AI is the #1 platform for finding and building careers in AI training and data labeling. We help freelancers discover specialized AI training work, build a unified portfolio, and turn annotation and review assignments into lasting professional experience.

For this role OpenTrain AI is the hiring and contracting organization. You will join a network of contributors who directly shape how modern AI systems learn from real-world professional practices.

About AI training and finance workflow review

AI training (data labeling, annotation, human feedback) is the human side of building AI: people prepare, evaluate, and refine the examples models learn from. Finance workflow review is a specialized strand of that work where reviewers check whether tasks mirror real professional practice in quantitative finance.

This role focuses on text-based evaluation: reading task instructions and context, judging realism, spotting missing assumptions or impractical constraints, and delivering concise, actionable feedback to improve dataset quality and downstream model behavior.

The role

OpenTrain AI is recruiting Finance Workflow Realism Reviewers to evaluate task inputs, expected workflows, and constraints for realism, clarity, scope, and internal consistency. This is a part-time contractor role requiring at least 20 hours per week.

Work is paid hourly at a competitive rate between $170 and $220 USD per hour, depending on experience and seniority. All reviews are text-based and use evaluation ratings plus short written recommendations.

• Employment type: Contractor, Part-time
• Time requirement: 20+ hours/week
• Pay: $170–$220 USD per hour (hourly)
• Language: Strong written English required
• Work type: Remote; text-based evaluations and short written feedback

What you'll do

• Review finance tasks and workflows for professional realism, clarity, scope, and internal consistency.
• Assess whether task inputs, context, and expected workflow match real practice in quantitative research, systematic trading, and portfolio construction.
• Identify missing assumptions, ambiguous instructions, unsupported requirements, impractical constraints, and material edge cases.
• Flag tasks that are too broad, too generic, or unlikely to produce a meaningful signal of real finance capability.
• Provide concise written feedback and targeted recommendations to improve task realism and signal quality.

Requirements

OpenTrain AI hires reviewers across two experience tracks. You must meet one of the following experience ranges and possess the stated domain expertise.

• Researcher track: 5–8 years of relevant professional experience in quantitative research, systematic trading, portfolio construction, or a closely related field.
• Senior reviewer track: 8–15 years of relevant professional experience; experience reviewing analysts, approving work, designing controls, or setting quality standards is especially valuable.
• Strong Python skills and hands-on experience building or reviewing quantitative research workflows.
• Deep understanding of research bias, data quality, validation, transaction costs, risk, and implementation constraints.
• Excellent written communication and the ability to explain judgments clearly.
• Strong written English and clear professional judgment are required.

Who should apply

You should apply if you have substantial, hands-on quantitative finance experience and enjoy turning domain knowledge into clear, actionable feedback that improves dataset quality and model behavior.

Ideal candidates combine technical fluency in Python and quantitative methods with experience evaluating or supervising research output, and they are comfortable making succinct, well-supported judgments in writing.

How it works

You'll receive text-based review tasks through the OpenTrain platform and submit evaluation ratings plus short written recommendations. Tasks focus on realism and actionable fixes rather than lengthy reports.

Compensation is hourly and tracked through OpenTrain AI; exact hourly placement within the $170–$220 range will be determined by experience and track. This role is contract-based and part-time, with flexible scheduling to fit your availability.

• Data type: TEXT; label types: evaluation ratings and short text feedback.
• Applications must be in English; strong written English is required.
• This role is not open to applicants located in: CU, IR, KP, SY, RU, BY, VE.