Financial Analyst Talent Network

$60–180/hrrate as listed

Mercor maintains a standing pool of financial analysts who build and grade tasks used to train and evaluate frontier AI models on modeling, forecasting, and statement analysis. You apply once, pass an AI interview, and get matched to contract projects as labs request finance expertise — typically 15–30 hours per week at observed rates of $60–180/hr.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

What the work actually looks like

This is not a single assignment. Mercor is building a bench of finance practitioners it can draw on when an AI lab commissions work in the domain. When a project lands, the day-to-day usually falls into a few shapes: writing realistic analyst tasks (a three-statement build from a messy 10-K, a variance bridge, a DCF with defensible assumptions), producing a gold-standard answer, then grading model attempts against it. Other engagements are pure evaluation — you read two model outputs and judge which one an actual FP&A lead would accept, and write out why.

The hard part is rarely the finance. It is articulating judgment that has become automatic: why a working-capital assumption is unsupportable, where a model quietly double-counted, why a technically correct number is still the wrong answer for the question asked. Written rationale carries as much weight as the numeric result.

What the screen is looking for

Verifiable professional history — where you did modeling, on what asset classes or business lines, at what level of ownership over the output
Depth that survives follow-up questions; the AI interviewer will push on a claim rather than accept it
Precision in explaining a method, not just naming it
Evidence you can work unsupervised against a rubric and hit deadlines without a manager

Candidates who describe responsibilities in general terms tend to stall. Candidates who can walk through one specific model they built, its assumptions, and what broke, tend to move forward.

Logistics

Fully remote and largely asynchronous, with work scheduled around your own hours. Typical project commitments run 15–30 hours per week and last weeks to months; matching is rolling, so there is often a gap between acceptance into the network and a first invitation. Rates in the $60–180/hr range have been observed across finance engagements and vary by project complexity and seniority — they are set per engagement, not guaranteed by network membership.

What they look for

Professional experience in financial modeling and forecasting
Financial statement analysis (three-statement, 10-K/10-Q)
Budgeting and variance analysis, ideally in an FP&A setting
Clear written explanation of quantitative reasoning
Ability to work independently and asynchronously against a rubric
Bachelor's degree in finance, accounting, economics, or equivalent professional track record

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Do you have at least two years of professional work experience in financial modeling, financial statement analysis, budgeting, forecasting, or a closely related finance role?
Professional finance experience · required
Are you located in a country where Mercor operates (including the US, EU, Canada, UK, Australia, and selected others; confirm if unsure)?
Remote work location · required
Do you have a resume or CV documenting your financial analysis experience that you can upload?
Resume and work history · required
Can you commit to 15-30 hours per week of remote work on a flexible schedule for AI evaluation projects?
Time availability · preferred

0 of 4 answered

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Whether the candidate has genuine hands-on modeling experience and understands the gap between assumption and outcome, not just theoretical knowledge of Excel functions.
A strong answer: The answer must name a specific model (revenue forecast, cash-flow projection, budget scenario), identify 2–3 key drivers or assumptions they controlled, and describe at least one point where actual results diverged from the forecast—showing they track forecast accuracy.
Where people lose it: Describing a model they watched someone else build, or naming only the tool (Excel, Python) without explaining what business question the model answered or how assumptions played out in reality.