We are building expert-authored forecasting and research data used to evaluate and improve AI models on real financial analysis. Each task asks a practitioner to reason from a fixed evidence cutoff — no hindsight — and document the judgment a strong analyst would have made at that moment.
This role covers single-name equity analyst publications: predicting when a named sell-side analyst will publish after a catalyst, and what that update will contain.
Backgrounds we look at first: Institutional Investor-ranked teams and leading research franchises such as Goldman Sachs, Morgan Stanley, JPMorgan, Bank of America, Citi, UBS, Barclays, Bernstein, Jefferies and Wolfe Research. Former colleagues, direct coverage rivals, and senior buy-side consumers of a named analyst's work are especially valuable. Brand-name experience is a strong first-pass signal but is not a hard requirement — direct experience and performance on the work sample determine who qualifies.
Every task is graded on reasoning from the stated cutoff without hindsight, data leakage, confidential information, or material non-public information.
We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
Mercor partners with leading AI labs and enterprises to train frontier models using human expertise. You will work on projects that focus on training and enhancing AI systems. You will be paid competitively, collaborate with leading researchers, and help shape the next generation of AI systems in your area of expertise.