CME Group is the world’s leading derivatives marketplace — but our fastest-growing frontier is data and analytics. With trillions in notional trading across our markets, CME produces one of the richest financial datasets on the planet: full-depth order books, microstructure signals, volatility surfaces, curve dynamics, order flow distributions, term-structure shifts, and real-time global risk transfer behavior. Through our strategic partnership with Google Cloud Platform (GCP), we now have unmatched compute power to analyze this data at scale and build the next generation of analytics, financial mathematical models, and AI systems. This internship gives you direct access to that ecosystem. About the Role We are seeking a PhD student-level intern (20 hours/week, part-time, 1–2 days onsite in Chicago) for a 12-month appointment working directly with the Executive Director leading CME’s Data & Analytics buildout. This is a ground-floor opportunity to contribute to both the modernization of CME’s core analytics and the development of new AI-driven, mathematically rigorous models. The role has three interlocking pillars: 1. Rebuilding & Operationalizing CME’s Financial Mathematical Models CME has a deep library of proprietary models across asset classes — some long-standing and widely used, others emerging. You will help: Review and where appropriate suggest improvements to analytical models using modern numerical and statistical methods Document and validate models in alignment with governance and regulatory standards Work with technology to prepare models for deployment using GCP infrastructure Help to document and at times direct conversion of legacy codebases into robust, maintainable analytics libraries Ensure mathematical transparency, reproducibility, and version control Collaborate with Product, Clearing, Data Science, Index, and Engineering teams Examples of model domains include: Curve construction & interpolation Volatility modelling (e.g., SABR in depth, SVI, spline surfaces, curve fitting) Option pricing & Greeks (finite-difference / Monte Carlo) Microstructure analytics (order book modeling, liquidity metrics) Risk models (scenario generation, historical VaR, CVaR, distribution modelling) Statistical estimation for high-frequency data Pros: Rare exposure to enterprise-scale quant model development Hands-on work with real market datasets, not simulated data Opportunity to improve models used by global financial institutions Cons: High expectations for precision, mathematical clarity, and documentation Requires comfort with governance and validation standards 2. Building New Machine Learning, Embedding, and Agent-Based Models You will help shape the next generation of CME’s AI capabilities, including: ML models trained on massive historical market datasets Embedding models for numerical, textual, and transactional data Agent-based systems and agent-communication protocols Market microstructure simulations powered by intelligent agents Predictive analytics and anomaly detection frameworks Hybrid models combining financial mathematics with ML architectures This work sits at the intersection of quant research and state-of-the-art AI — and will be developed directly on GCP, leveraging tools such as BigQuery, Vertex AI, and large-scale notebooks. Pros: Frontier-level ML exposure with real, large datasets Creative freedom on prototypes Real influence on CME’s long-term AI strategy Cons: Ambiguity: some initiatives start from a blank page Must be comfortable iterating quickly and defending methodological choices 3. Quant Research, Data Engineering, and Cross-Functional Collaboration You will also: Conduct quantitative research across CME’s datasets Build analytical pipelines using Python + GCP tooling Develop visualizations and explainers for internal and client use Support monthly and quarterly research themes Present written and verbal findings to senior leadership Help shape best practices for model governance, testing, and production readiness