Moreton Capital Partners

Role Description As Quantitative Analyst, you will drive the research process that underpins our prediction markets trading strategies. You will develop alpha signals, build and validate models, and work closely with our traders and engineers to take research from idea to live deployment. You will own projects end to end — from data ingestion and exploratory analysis through to implementation, testing, and performance monitoring. We operate across a broad event universe including professional sports, macroeconomics, geopolitics, climate, and financial markets. Research breadth and the ability to develop domain expertise quickly are assets here. 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. Qualifications - 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. Who Thrives Here The best Quantitative Analysts at MCP combine academic rigour with genuine curiosity — they read papers because they want to, follow market resolutions because they are interesting, and build things outside of work because they cannot help it. We value intellectual honesty, the ability to kill your own ideas when the data says so, and the drive to turn good research into production-quality work. This role is well suited to a researcher who wants to see their work actually trade. Every model you build has a clear path to live deployment, and you will have direct visibility into how your research performs in the market. How to Apply Send your CV and a cover letter that demonstrates your genuine engagement with prediction markets and quantitative research. We want to understand what you have built, what you have studied, and how you think about identifying and validating an edge. Include links to any relevant work: GitHub repositories, research write-ups, Kaggle or competition work, or personal projects. Applications reviewed on a rolling basis. Strong candidates will complete a technical exercise focused on real prediction market data, followed by a research discussion with the team. Compensation Base salary commensurate with experience, performance-linked bonus. We will discuss specifics with the right candidate.

Role Description As a Trader, you will execute and actively manage a live portfolio of prediction market positions. You will develop and own a suite of strategies, reporting into a Portfolio Manager and accessing quant analysts to continuously refine our edge. This is an execution-first role: you are on the tools, managing live positions, and responsible for real P&L. We are equally interested in proven practitioners and sharp, self-directed candidates earlier in their careers who can demonstrate they have already been doing this seriously on their own. Key Responsibilities - Monitor and manage live positions across Polymarket (CLOB, Gamma, Subgraph APIs) and Kalshi (REST, WebSocket) throughout the trading day. - Execute strategies including: - Dynamic market making with real-time skew adjustment - Order-flow and book imbalance exploitation - Cross-platform pricing arbitrage - News and event momentum - Mean-reversion on statistically related contract pairs - Build and backtest quantitative models using historical tick data, Bayesian probability frameworks, NLP-driven news parsing, and ML-based fair value estimation. - Collaborate with engineering on execution infrastructure — API integrations, order routing, position monitoring, and anomaly detection. - Maintain thorough post-trade analysis: attribution, model performance, slippage, and strategy decay monitoring. - Contribute ideas to the broader research agenda — prediction markets sit within MCP's wider effort to apply agentic AI and multi-signal frameworks to event-driven alpha. - Direct, hands-on experience trading on Polymarket and/or Kalshi — via personal accounts, proprietary systems, or professional roles. We expect specifics: markets traded, strategies used, edges identified. Qualifications - Strong Python skills: pandas, NumPy, backtesting frameworks, and API integrations built from scratch. - Solid foundation in probability, statistics, time-series analysis, and Bayesian inference. - Genuine, demonstrable engagement with the prediction markets ecosystem. This is a hard filter: we will ask for specifics about recent resolutions, platform mechanics, and structural observations. - Undergraduate or higher degree in a quantitative discipline, or equivalent demonstrated ability. Bonus Points For - A live profitable track record in prediction markets, options, or event-driven strategies — even at personal scale. - Experience with blockchain and DeFi tooling: Polygon RPC, USDC wallet management, ethers.js or equivalent. - Familiarity with low-latency execution, FIX protocol, or Web3 API infrastructure. - Domain depth in high-volume event categories: professional sports analytics, macroeconomics, geopolitics, climate. - Interest in or experience with agentic AI applied to trading — an active investment area at MCP. Who Thrives Here The Trader role at MCP suits someone who runs hot on markets — who monitors Polymarket and Kalshi not because they have to, but because they cannot help it. If you have been building personal models, studying resolution patterns, or finding edges in thin markets on your own time, you are exactly who we want to talk to. We are as interested in exceptional self-starters earlier in their careers as we are in experienced professionals. How to Apply Send your CV and a cover letter that goes beyond generic interest. Tell us specifically: markets you have traded, strategies you have run, edges you have found, and what you see as the clearest structural opportunities available today on Polymarket or Kalshi. Include links to any relevant work — GitHub, personal research, or portfolio evidence. Applications reviewed on a rolling basis. Strong candidates will progress through a technical screen, strategy discussion, and a live modelling or coding exercise. Performance-linked compensation. Details discussed at the offer stage.

Role Description This is a senior leadership position for MCP's prediction markets strategy. You will run the investment process end to end: strategy, portfolio construction, risk management, and performance. You will build and lead a global team of traders, and you will work closely with MCP's quant research and engineering functions to develop proprietary models and infrastructure that compound our edge over time. We are looking for someone who has operated a real book — who understands market microstructure, manages drawdowns with discipline, and knows how to extract consistent expectancy from volatile, event-resolution-driven markets. This is not a research role that touches a portfolio occasionally; you are accountable for P&L. Key Responsibilities - Own the prediction markets P&L. Set strategy, manage risk limits, oversee position sizing, and ensure the portfolio operates within defined drawdown and exposure parameters at all times across all traders. - Lead the full investment process: idea generation, model validation, live execution, post-trade analysis, and systematic improvement cycles. - Build and manage a team of traders and analysts. Set research priorities, review work rigorously, and develop talent. - Define and evolve MCP's edge across strategy types: market making, cross-platform arbitrage, event-driven momentum, statistical modelling on correlated contracts, and alternative data signal development. - Work with engineering to spec and prioritise infrastructure: execution systems, data pipelines, monitoring tools, and low-latency order management. - Contribute to investor reporting and, where relevant, capital raising — articulating the strategy's edge, risk profile, and performance clearly to institutional audiences. Qualifications - Significant experience managing a live quantitative or event-driven trading portfolio with genuine P&L accountability — not just advisory or model-development roles. - Demonstrated edge in prediction markets, binary event trading, options, or closely related event-resolution domains. - Strong quantitative foundation: probability, Bayesian inference, statistical modelling, and portfolio construction. - Experience managing or mentoring junior trading professionals. - Clear, substantive engagement with the prediction markets ecosystem — platforms, mechanics, recent resolutions, structural inefficiencies. - Undergraduate or postgraduate qualification in a quantitative discipline. Bonus Points For - Experience operating on Polymarket, Kalshi, or equivalent platforms at meaningful scale. - Background in systematic commodity, macro, or multi-asset trading — our core competency and an area of genuine cross-pollination with prediction markets. - Familiarity with the analytics and data ecosystems around professional sports and major real-world event categories (politics, macroeconomics, climate). - Experience with blockchain/DeFi infrastructure relevant to on-chain prediction market platforms. - Prior involvement in fund-level processes: investor relations, risk reporting, or strategy documentation. How to Apply Send your CV and a covering note that speaks specifically to your prediction markets experience — strategies you have run, edges you have identified, how you think about managing risk in thin event-resolution-driven markets, and why this opportunity is the right next step for you. Applications reviewed on a rolling basis. Strong candidates will have a strategy discussion with MCP's senior team, followed by a detailed investment process review. Competitive base salary, meaningful performance-linked upside, and participation in MCP's broader success. Structure and quantum discussed at the offer stage with the right candidate.