Machine Learning Intern, Regulatory

Job Description

Building trusted markets — powered by our people.

At Cboe Global Markets, we inspire our people to solve complex challenges together because what we do matters. We provide the financial infrastructure that powers the global economy. As a leading provider of market infrastructure and tradable products, Cboe delivers cutting-edge trading, clearing and investment solutions to market participants around the world.

Cboe interns work with a variety of staff across multiple departments and have the opportunity to put their skills to work in their field of interest, while learning about Exchange technology and operations through our robust Options Institute courses.

The three main foundational pillars of our internship program are: develop, educate and network. We want to ensure each of our interns receive a real-world working experience that encourages academic, professional and personal growth.

Candidates should be versatile, eager and able to work in a fast-paced, time-sensitive financial and technical environment. Our interns will have the flexibility of working 2 days remotely, and 3 days in office per week at one of our state-of-the-art offices in Chicago, Kansas City, and New York City. To be eligible for this internship, applicants must be enrolled in a university or college program and should not be scheduled to graduate before December of the year in which the internship takes place. Our internship program runs from June to August, and you will wrap up your internship with a final presentation and retreat. Visit our student page for more information about our internship program!

The Regulatory Technology team is hiring a Machine Learning intern:

The Machine Learning intern at Cboe will work on prototyping, training, testing and validating ML models and applications for the surveillance of financial markets, which generate terabytes of new data and trading patterns every day. The successful candidate will have extreme intellectual curiosity, perseverance, good academic knowledge of machine learning models, statistical techniques and will demonstrate strong programming and large-scale data engineering skills. Together, let’s invent new techniques to detect violative behavior in financial markets.

Your responsibilities and learning objectives will be:

• Train various candidate models to fit a given business problem
• Prepare data sets and design data features for ML input.
• Effectively track and evaluate ML model performance during research phase.
• Contribute creative ideas to problem solve with ML techniques during brainstorming discussions with ML team and users
• Receive and implement constructive feedback through rigorous code reviews and QA testing.
• Work in both on-premises and cloud environments.
• Produce clear and thorough documentation for your research and analytical work.
• Communicate technical information clearly and concisely to an end-user audience.
• Learn best practices in software engineering and ML research

The ideal candidate has:

• Understanding of a wide variety of machine learning algorithms, supervised and unsupervised, classical and deep learning and their unique infrastructure requirements
• Familiarity with machine learning best practices in training, validation, inference, and monitoring
• Strong Python-based programming and data engineering skills
• Strong SQL knowledge
• 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
• Bachelor’s or Master’s degree in progress in a quantitative field and should not be scheduled to graduate before December of the year in which the internship takes place.

You’ll really stand out with:

• Knowledge of advanced undergraduate-level mathematics, including statistics, linear algebra, and multivariable calculus
• Knowledge of relevant graduate-level mathematics, such as stochastic methods and numerical analysis will be a huge plus
• Experience in production software development environments, including version control

automated testing, and change management would be a great plus

• 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

Benefits and Perks

• Competitive compensation