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Local Political Expert — Elections Forecasting & Political Risk

Mercor · 100% remote · Contract · Posted

Pay
$100–200/hr
Location
Worldwide
Languages
English
Hours
Flexible
Openings
Not listed
Level
Expert
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Summary

Author point-in-time political reports and forecasts on elections and political risk, make directional judgments on polling and win probabilities, and review and grade model-generated analyses against your own standard.

What you'll do

  • Author point-in-time political reports and forecasts from a stated evidence cutoff
  • Make and document directional judgments about polling, win probabilities, and market-implied odds
  • Explain the causal mechanism behind an event's expected political and market impact
  • Review model-generated analyses and grade them against your own standard

Requirements

  • Have hands-on experience on specific races in swing states
  • Demonstrate on-the-ground knowledge of the electorate, candidates, and local political environment
  • Show a record of making live judgments about polling, win probabilities, or odds in those races
  • Possess strong polling literacy and causal event analysis
  • Have typically 8+ years of direct experience or equivalent performance

Skills

  • Political Forecasting
  • Election Analysis
  • Polling Literacy
  • Causal Event Analysis
  • Probabilistic Judgment
  • Political Risk Assessment

Full description

About the work

We are building expert-authored forecasting and research data used to evaluate and improve AI models on real political and 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 political report authorship: elections, polling, and political-risk events.

What you will do

  • Author point-in-time political reports and forecasts from a stated evidence cutoff

  • Make and document directional judgments about polling, win probabilities, and market-implied odds

  • Explain the causal mechanism behind an event's expected political and market impact

  • Review model-generated analyses and grade them against your own standard

Who we are looking for

  • Local political analysts with hands-on experience on specific races in swing states: current or upcoming Senate, governor, or statewide contests, or recent cycles in the same state

  • On-the-ground knowledge of the electorate, the candidates, and the local political environment, from roles such as university political scientist, state or regional pollster, state-level campaign or party analytics staff, or state political-risk analyst

  • A record of making live judgments about polling, win probabilities, or odds in those races — not retrospective commentary

  • Strong polling literacy and causal event analysis

  • Typically 8+ years of direct experience, or unusually strong evidence of equivalent performance

Backgrounds we look at first: political science departments and survey research centers at universities in the target states, state and regional pollsters, state-level campaign and party analytics teams, and decision desks or race-ratings outlets with state-specific coverage. National forecasting or political-risk experience is welcome when paired with deep knowledge of a specific state. 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.

Not a fit

  • Profiles whose experience is mainly international politics, geopolitics, or country risk, with little or no work on US races

  • Analysts who have not worked on a current or recent US race in the state they would cover

  • Pure communications or journalism profiles without demonstrated forecasting, polling, or probabilistic judgment

  • Academic profiles whose work is purely theoretical, with no analysis of live races

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.

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