Quantitative Analyst - Prediction Markets

Moreton Capital Partners

About The Position

Moreton Capital Partners (MCP) is a CFTC regulated systematic, technology-first investment manager trading across commodities, alternative markets, and emerging data-rich asset classes. We combine quantitative modelling, agentic AI, and deep domain expertise to build strategies with durable, uncorrelated alpha. We are building a dedicated prediction markets fund and are looking for a Quantitative Analyst to own the research function within that team. This is a ground-up role: you will have genuine ownership of the work, direct access to senior decision-makers, and the opportunity to see your models run live in a real portfolio.

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.

Nice To Haves

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.

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.
Build 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.