Quantitative Analyst - Prediction Markets

Responsibilities

• Conduct rigorous quantitative research to identify new alpha signals across prediction market categories — sports, macro, political, financial, and environmental events
• Own the end-to-end research process in close collaboration with the Portfolio Manager: data sourcing and ingestion, exploratory analysis, methodology design, implementation, backtesting, and live performance evaluation
• Develop and maintain data pipelines drawing on alternative and traditional data sources — market microstructure, public resolution data, news and sentiment feeds, sports analytics databases, and fundamental datasets
• Develop and improve models for fair value estimation, calibration analysis, and systematic strategy construction
• Extend and improve MCP's internal research platform — tools, libraries, and workflows that make the whole team faster and more rigorous
• Maintain a systematic review of the academic and practitioner literature on prediction markets, sports analytics, Bayesian forecasting, and related fields
• Produce clear, structured research outputs — documented methodology, performance attribution, and actionable recommendations — that can be directly used by traders

Requirements

• Undergraduate or postgraduate degree from a strong institution in data science, computer science, mathematics, statistics, operations research, financial engineering, or a closely related quantitative field
• Strong Python skills: pandas, NumPy, scikit-learn, and experience building backtesting or research frameworks from scratch
• Solid foundation in statistics, probability, time-series analysis, and machine learning — with the ability to apply these rigorously rather than just use libraries
• Demonstrated interest in prediction markets — personal trading, research, protocol analysis, or equivalent engagement. We expect you to know these platforms well
• Ability to work independently and take full ownership of a research workstream, not just execute tasks handed to you

Bonus Points

• For two or more years of experience in a data-driven research environment with a focus on model development and forecasting — though we will consider exceptional candidates at earlier career stages
• Familiarity with Polymarket and/or Kalshi platform mechanics, resolution data, and API access
• Experience with NLP, sentiment analysis, or unstructured data processing applied to financial or event-driven contexts
• Comfort with agentic AI frameworks and LLM-based research tooling — MCP is actively investing in this area
• Knowledge of Bayesian methods and their application to probability calibration and forecast updating
• Experience with blockchain data or on-chain analytics tools relevant to decentralised prediction market platforms