Finance Expert — Quant Research & Systematic Trading

About the Opportunity

A leading AI research organization is starting a focused Finance Sprint within Project Mercury in the next 24 hours. We are seeking experienced finance professionals to evaluate whether individual workflows and tasks accurately reflect real professional practice in their specialist domain.

Offers are first-come, first-served. This will be a high-engagement project over the next week, so please apply only if you can stay actively involved throughout the sprint. Work may involve completing multi-hour tasks in one sitting, typically around 2–4 hours, and requires keeping Slack notifications on and responding to project updates, questions, and feedback within the day.

This is a workflow-realism evaluation engagement. You will use your professional judgment to assess whether each task, its provided context, expected workflow, and constraints would make sense in real practice. The exact task content is confidential and may vary during the sprint.

What You'll Do

• Review individual finance tasks and workflows for professional realism, clarity, scope, and internal consistency
• Assess whether the task inputs, context, and expected workflow reflect how the work is actually performed in your domain
• Identify missing assumptions, ambiguous instructions, unsupported requirements, impractical constraints, and material edge cases
• Flag where a task is too broad, too generic, or otherwise unlikely to produce a meaningful signal of real finance capability
• Provide concise, structured written feedback and targeted recommendations to improve task realism
• Where needed, cross-check task logic with trusted domain peers to maintain a high bar across the sprint

Who We're Looking For

• 5–8 years of relevant experience for the researcher track, or 8–15 years for the senior reviewer track
• Professional experience in quantitative research, systematic trading, portfolio construction, or a closely related field
• Strong Python skills and hands-on experience building or reviewing quantitative research
• Deep understanding of research bias, data quality, validation, transaction costs, risk, and implementation constraints
• Excellent written communication and the ability to explain not only whether something is correct, but why
• High attention to detail, sound professional judgment, and comfort evaluating work against explicit standards
• Experience reviewing analysts, approving work, designing controls, or setting quality standards is especially valuable for the senior track

Why This Work Matters

High-quality AI evaluation depends on tasks that reflect genuine professional practice, not just plausible-looking work. Your expertise will help ensure this sprint measures meaningful finance capability with rigor and consistency.