Cboe Global Markets is the world's go-to derivatives and exchange network, providing trading solutions and products in multiple asset classes, including equities, derivatives, FX, and digital assets. Cboe's Regulatory Division directly contributes to the company's success by promoting fair, transparent, and trusted markets, through effective and efficient market oversight. We operate surveillance, examination, and investigative programs aimed at detecting and disciplining, or preventing, violative behavior. As a Machine Learning Engineer - Regulatory, you'll prototype and implement ML models and applications for the surveillance of financial markets, which generate terabytes of new data every trading day. The successful candidate will deeply understand machine learning algorithms and best practices, will demonstrate strong programming and large-scale data engineering skills, and will have broad experience with production software development environments. Together, let's turn data into actionable insights.

Location: Flex hybrid in Chicago IL or Lenexa KS

• Prototype and implement machine learning models and applications for the surveillance of financial markets
• Partner with financial analysts and investigators to define and refine functional requirements
• Develop efficient pipelines for processing terabytes of new financial data generated every trading day
• Prepare data sets and design data features for ML input
• Develop explainability solutions for a highly regulated, audited, and governance-conscious environment
• Effectively track and evaluate ML experiments
• Monitor and maintain production ML deployments
• Design and develop production-quality, test-driven software code, written primarily in Python
• Provide and receive constructive feedback through rigorous code reviews and QA testing
• Work in both on-premises and cloud environments
• Work closely with complementary engineering teams
• Produce clear and thorough documentation, including high-level charter, functional requirements, technical design, and testing scenarios
• Provide ongoing application support to end users
• Communicate technical information clearly and concisely to an end-user audience

• Bachelor's degree in a quantitative field
• 3 years of relevant professional experience
• Knowledge of advanced undergraduate-level mathematics, including statistics, linear algebra, and multivariable calculus
• Deep understanding of a wide variety of machine learning algorithms, supervised and unsupervised, classical and deep learning
• Familiarity with machine learning best practices in training, validation, inference, and monitoring
• Strong Python-based programming and data engineering skills, and strong SQL skills
• Experience in production software development environments, including version control
• automated testing, and change management
• Experience with common data science and ML frameworks, such as numpy, pandas, Spark, scikit-learn, TensorFlow, and PyTorch
• Experience with large-scale data sets
• Ability to work both independently and as part of a team
• Excellent written and verbal communication skills
• Demonstrates critical thinking, attention to detail, and good judgment

• Master's degree in a quantitative field
• Knowledge of relevant graduate-level mathematics, such as stochastic methods and numerical analysis
• Experience with test-driven development
• Experience with distributed ML frameworks
• Familiarity with AWS
• Familiarity with Snowflake
• Experience in the financial services sector
• Experience in highly regulated industries
• Familiarity with other relevant programming languages, such as Scala, Java, and C

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Any communication from Cboe regarding this position will only come from a Cboe recruiter who has a @cboe.com email or via LinkedIn Recruiter. Cboe does not use any otherthird party communication tools for recruiting purposes.