Quantitative Researcher​/Trader — Prediction Markets

Trireme is a digital-asset trading, market-making and quantitative research firm operating across crypto markets, systematic trading and emerging financial-market structures.

We are expanding our quantitative research capability into prediction markets and event-driven trading.

We are looking for a Quantitative Researcher / Trader who can develop systematic strategies for markets where prices represent the probability of real-world outcomes.

You will research, build and operate quantitative strategies across prediction markets.

The role requires a combination of:

• Probability and statistics.
• Quantitative research.
• Market microstructure.
• Event-driven trading.
• News and information processing.

You should be capable of thinking about markets as probability distributions rather than simply directional price charts.

The objective is to identify situations where implied market probabilities materially differ from the probability of an event occurring, and determine whether that difference represents genuine edge after liquidity, execution costs, settlement mechanics and model uncertainty are considered.

• Research systematic and discretionary-assisted strategies across prediction markets.
• Build models estimating probabilities of real-world events.
• Compare model-derived probabilities against market-implied probabilities.
• Identify and quantify mispricing.
• Develop event-driven signals using structured and unstructured data.
• Build data pipelines for relevant economic, political, financial, sporting or other event data.
• Research cross-market and cross-platform arbitrage opportunities.
• Analyse order books, spreads, liquidity and market depth.
• Model expected value and position sizing.
• Develop execution strategies appropriate for relatively illiquid markets.
• Monitor changing information and update probability estimates dynamically.
• Build systems for rapid response to material news or data releases.
• Research market-maker behaviour and participant biases.
• Analyse historical calibration of prediction markets.
• Conduct rigorous backtesting where historical market data is available.
• Build simulation frameworks where traditional backtesting is not possible.
• Monitor live strategy performance.
• Maintain clear P&L and attribution by strategy and event.
• Develop risk controls around correlated event exposures.
• Monitor platform, counter party and settlement risk.
• Document model assumptions and sources of uncertainty.
• Work closely with engineers to automate data ingestion, pricing and execution.

Research may include:

• Elections and political events.
• Macroeconomic releases.
• Regulatory events.
• Crypto-specific events.
• Token launches and protocol milestones.
• Financial-market outcomes.
• Geopolitical events.
• Sports or other highly liquid event markets.
• Cross-platform probability discrepancies.
• Prediction-market versus traditional-market pricing.
• News sentiment and information extraction.
• Poll aggregation.
• Forecast combinations and ensemble models.
• Market calibration and participant behavioural biases.

The specific markets traded will depend on liquidity, legality, platform access, risk and demonstrated edge.

Strong candidates should be comfortable with concepts including:

• Conditional probability.
• Bayesian inference.
• Calibration.
• Expected value.
• Logistic models.
• Time-series analysis.
• NLP and information extraction.
• Correlated outcomes.

The ability to assign uncertainty to your own probability estimate is as important as producing the estimate itself.

If a market implies a 40% probability of an event occurring, it is not enough to conclude that the contract is attractive because your model predicts 50%.

You should be able to explain:

• Why your estimate is 50%.
• The confidence interval around that estimate.
• Which assumptions drive the discrepancy.
• Whether those assumptions are already reflected in the market.
• How quickly your informational advantage is likely to decay.
• Expected slippage.
• Settlement and platform risk.
• Correlation with the existing portfolio.
• The conditions that would invalidate the trade.

• Strong background in quantitative research, statistics, mathematics, computer science, economics, finance, physics or another analytical discipline.
• Excellent probability and statistical reasoning.
• Strong Python.
• Experience working with financial or event-driven datasets.
• Ability to build models from incomplete and noisy information.
• Strong understanding of expected value and…