Join OpenTrain as a Finance Workflow Realism Reviewer for a high-engagement, short sprint evaluating whether finance tasks match real professional practice; contractor, part-time work at $210–$270/hr, ~20+ hours/week, English required.
Legal & Finance
Remote Hourly · $210–$270/hr
$210–$270/hr
Compensation
7 countries
Eligibility
Expert
Experience
Jul 29, 2026
Posted
Open to applicants in
Cuba Iran North Korea Syria Russia Belarus Venezuela
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OpenTrain is the centralized platform where people start and grow careers teaching AI through high-quality AI training and data-labeling work. We connect skilled contributors with short-term and ongoing projects that directly shape how modern AI systems behave.
About AI training work
AI training is the human expertise behind machine learning: people prepare, evaluate, and correct examples so models learn useful, reliable behavior. This role is part of that ecosystem—experienced finance practitioners help ensure tasks used to train finance-focused AI are realistic, clear, and reliable.
Role overview
You will review written finance tasks and workflows to judge whether they reflect real professional practice, then deliver concise, structured feedback and actionable recommendations to improve task realism and signal quality.
This is a high-engagement contractor role running as a sprint over the next week. Tasks may require a few hours at once and demand active responsiveness during the sprint.
Contract type: Contractor, part-time
Time: 20+ hours/week during a concentrated 1-week sprint
Rate: USD $210–$270 per hour (PAY_PER_HOUR)
Data type: Text review and evaluation (label type: EVALUATION_RATING)
Language: English required
What you'll do
Review finance tasks and workflows for clarity, scope, realism, and internal consistency
Assess whether inputs, context, and expected steps match real professional practice
Identify missing assumptions, ambiguous instructions, impractical constraints, and edge cases
Flag tasks that are too broad, generic, or unlikely to produce meaningful signals of finance capability
Deliver concise, structured written feedback and targeted recommendations to improve task realism
Cross-check task logic with trusted domain peers when necessary to maintain quality
Requirements
5–9 years relevant experience for practitioner-level work, or 9–15 years for senior-reviewer work
Professional background in fundamental equity research, asset management, hedge funds, or a closely related investing role
Strong financial-statement analysis, forecasting, valuation, and investment-judgment skills
Excellent written communication and the ability to explain why work is or is not correct
Strong attention to detail and comfort evaluating work against explicit standards
Experience reviewing analysts, approving work, designing controls, or setting quality standards is especially valuable for senior reviewers
Helpful background
Experience working on research workflows that demand rigorous judgment and detailed written critique will help you be effective quickly.
Exposure to TMT or semiconductor coverage, event-driven research, or SEC-filings-intensive analysis is a plus but not required.
Logistics, compensation, and eligibility
Pay is hourly at USD $210–$270 per hour; the project is contractor, part-time, and requires active engagement during a concentrated sprint (expect 20+ hours in the week).
You will work on text-based task reviews and provide evaluation ratings plus written recommendations. English fluency is required.
Employment types: CONTRACTOR, PART_TIME
Time requirement: 20+ hours/week during sprint
Payment type: PAY_PER_HOUR (USD)
This role is not open to residents of: CU, IR, KP, SY, RU, BY, VE
Who should apply and next steps
Apply if you are an experienced finance practitioner who enjoys improving workflows, writing clear critiques, and applying professional judgment to make tasks realistic and testable.
To apply, create or use your OpenTrain profile and submit your résumé plus a brief summary of relevant experience and availability; you will be asked to respond promptly during the sprint.
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