Quantitative Researcher / Trader — Prediction Markets

Trireme; Greater London; Presencial; GBP 90.000 - 140.000; Jornada completa

Descripción de la vacante

Trireme is expanding its quantitative research capability into prediction markets and event-driven trading. The role requires building and operating systematic strategies where market prices reflect probabilities of real-world outcomes.

You will research statistics, market microstructure, and news-driven data to identify edge while accounting for liquidity, costs and model uncertainty. Candidates should be proficient in Python.

Formación

• Strong background in quantitative methods and statistical reasoning.
• Ability to build models from noisy data and incomplete information.

Responsabilidades

• Research and build systematic and discretionary strategies for prediction markets.
• Construct models estimating real-world event probabilities and compare with market prices.
• Develop data pipelines for econ, political, financial and other event data.
• Research cross-market arbitrage and mispricing opportunities.
• Analyze order books, liquidity, and market depth to inform sizing and risk controls.
• Monitor live strategy performance and update probabilities as new information arrives.

Conocimientos

Probability & statistics

Quantitative research

Market microstructure

Event-driven trading

NLP & information processing

Herramientas

Python

Pandas

Descripción del empleo

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.

The Role

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.

Responsibilities

• 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, counterparty and settlement risk.
• Document model assumptions and sources of uncertainty.
• Work closely with engineers to automate data ingestion, pricing and execution.

Potential Research Areas

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.

Quantitative Approach

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.

Example

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.

What We Are Looking For

• 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 uncertainty.
• Ability to distinguish genuine informational edge from narrative conviction.
• Intellectual honesty and willingness to update views as evidence changes.
• Ability to operate independently.

Highly Desirable

Experience with:

• Prediction markets.
• Polymarket or similar venues.
• Event-driven trading.
• Sports betting or quantitative betting markets.
• Forecasting competitions.
• NLP / LLM-based information extraction.
• Crypto market structure.
• Market making.
• Automated execution.

A successful background in quantitative sports betting can also be highly relevant given the overlap in probability estimation, pricing and market efficiency.

Candidate Evaluation

Candidates should be expected to complete a practical research exercise.

An example assessment could involve providing:

• A live or historical prediction market.
• Current market-implied probability.
• A set of relevant data sources.

The candidate would then be asked to:

• Produce an independent probability estimate.
• Explain the methodology.
• Identify the most important variables.
• Quantify uncertainty.
• Determine whether a trade exists.
• Recommend a position size.
• Define invalidation criteria.
• Explain the main risks.
• Describe how the model would update as new information arrives.

We care more about the quality of reasoning and calibration than whether the candidate happens to predict the correct eventual outcome.

Performance Measures

Success will be judged on:

• Calibration of probability forecasts.
• Expected value captured.
• Maximum drawdown.
• Forecast accuracy.
• Brier score or equivalent calibration measures.
• Quality of research.
• Repeatability of strategies.
• Speed and quality of information processing.
• Strategy scalability.
• Risk-limit adherence.

Prediction-market positions can contain significant hidden risks.

The role will be expected to explicitly manage:

• Correlated event exposures.
• Binary payout risk.
• Liquidity gaps.
• Platform and counterparty risk.
• Concentrated political or geopolitical exposure.
• Sudden probability repricing.

No strategy should move into meaningful production capital without a documented hypothesis, defined risk limits and evidence of positive expected value.

What Good Looks Like

A strong researcher in this role should eventually be capable of building a system where:

is increasingly systematic, measurable and automated.

Compensation

Competitive base or contractor fee with performance-linked upside.

For the right candidate, compensation may include a meaningful variable component linked to validated strategy performance and realised trading P&L, subject to agreed risk limits and loss-adjusted performance.