Mercornew this weekEquity Research Expert — Single-Name Analyst Publications
Good fit for: accountants, analysts, and finance professionals (CPA).
Listing verified on the platform’s official board. Last checked September 25, 2026.
About the work
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.
What you will do
- Forecast the timing and content of a named analyst's post-catalyst research note
- Reason from filings, earnings, transcripts, estimates, valuation and ratings to an expected published view
- Document the internal research workflow that determines how and when an equity research report gets produced
- Review model-generated analyses and grade them against your own standard
Who we are looking for
- A former lead sell-side equity analyst or highly senior research associate who personally published post-catalyst research in the exact sector
- Deep command of filings, earnings, transcripts, estimates, valuation, ratings, and the internal workflow behind an equity research report
- Typically 8+ years of direct experience, or unusually strong evidence of equivalent performance
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.
Point-in-time discipline
Every task is graded on reasoning from the stated cutoff without hindsight, data leakage, confidential information, or material non-public information.