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Research and Product Development PhD Student Intern
3 weeks ago
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
What You Bring
Required
PhD candidate in mathematics, statistics, physics, engineering, computer science, quantitative finance, econometrics, or a related field
Strong foundation in financial mathematics (stochastic calculus, derivatives modeling, numerical methods, or equivalent)
Proficiency in Python and scientific computing libraries
Ability to communicate complex concepts clearly in writing
Strong analytical discipline and attention to detail
Self-starter comfortable working across multiple business lines
Bonus
Experience with GCP: BigQuery, Vertex AI, Dataflow, C++
Experience with ML, embeddings, or agent-based systems
Background in market microstructure, derivatives, or high-frequency data
Prior publications, technical reports, or model documentation
Schedule & Structure
20 hours/week
1–2 days onsite in the Chicago office
12-month internship
Flexible to accommodate academic commitments
Direct mentorship and collaboration with an Executive Director leading CME's new Data & Analytics function
This is not a typical internship — you will be a core contributor to a strategic buildout.
Why This Is a Rare Opportunity for a PhD Candidate
Access to one of the deepest financial datasets in existence
Ability to work on both quantitative financial models and cutting-edge AI systems
Experience bringing models into enterprise-scale production
Direct contribution to CME's next-generation data analytics platform
Exposure to real governance, validation, and model-risk frameworks
A signature line on your CV that signals:
"I built models used by global markets."
CME Group: Where Futures are Made
CME Group is the world's leading derivatives marketplace. But who we are goes deeper than that. Here, you can impact markets worldwide. Transform industries. And build a career by shaping tomorrow. We invest in your success and you own it – all while working alongside a team of leading experts who inspire you in ways big and small. Problem solvers, difference makers, trailblazers. Those are our people. And we're looking for more.
At CME Group, we embrace our employees' unique experiences and skills to ensure that everyone's perspectives are acknowledged and valued. As an equal-opportunity employer, we consider all potential employees without regard to any protected characteristic.
Important Notice: Recruitment fraud is on the rise, with scammers using misleading promises of job offers and interviews to solicit money and personal information from job seekers. CME Group adheres to established procedures designed to maintain trust, confidence and security throughout our recruitment process. Learn more here.
