Lead Quantitative Analyst

Job Description:

Building trusted markets —powered by our people. 

At Cboe, 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. 

We’re building inclusive ways to support professional and personal development while strengthening the trust we’ve earned as a global market leader. Our teams are empowered to share ideas, actively pursue them and bring on a challenge. As champions of internal mobility and access to opportunity, we encourage our people to “go for it” and equip our managers with the training to coach their teams to the next level. Our Associate Resource Groups champion diversity, equity and inclusion, giving associates a safe space to network, share ideas and create opportunities.  

Sound like the place for you? Join us! 

Location: Chicago or NYC office (flex hybrid)

Overview:

We are seeking a Lead Quantitative Analyst with a strong product development mindset to analyze global real-time option models, applied to underlying equities and futures with dependencies on interest rate curves and FX. This role focuses on Research and Development, and Prototyping in new variations of our derivatives pricing models. The ideal candidate will have expertise in quantitative finance, data analytics, and machine learning, with a track record of modeling the application of financial models to market data.

Key Responsibilities:

1. Option Model Analysis & Research

• Analyze existing implementations of option pricing models and conduct empirical research into the efficacy over options of different geographies and liquidity levels.
• Apply statistical methods including classification models, regressions, and machine learning to identify features of derivatives that are most suited for each type of model.
• Collaborate with engineering teams to ensure effective and consistent quality in analytic pipelines.

2. Advanced Data Analytics & Machine Learning

• Apply machine learning techniques to detect patterns, anomalies, in applying Model Fitted Theoretical pricing to different categories of options.
• Utilize statistical modeling, regression analysis, and classification methods to improve calibration of Model Fitting methods.
• Implement feature engineering approaches to improve the quality of production applications of real-time Model Fitting that use approaches such as SABR, Mixture Models, or SVI.

3. Data Distribution & Market Engagement

• Collaborate with hedge funds, asset managers, and proprietary trading firms to understand their options analytics needs and commercial use cases.
• Assist sales and marketing teams in positioning and selling analytic data services to institutional clients.

Required Qualifications & Skills:

Education & Experience

• Master’s or PhD degree in Quantitative Finance, Computer Science, Data Science, Statistics, Engineering, or a related field.
• 6 years of experience with a Master’s degree or 3 years with a PhD in a quantitative, data science, or research role  at a financial institution such as a bank, fund manager, market maker, or a risk analytics vendor.

Technical & Analytical Skills

• Strong programming skills in Python (pandas, NumPy, scikit-learn) and SQL. Experience with R is a plus.
• Experience with Java. Strong Java skills are a plus.
• Experience with time-series analysis, and scalable computing.
• Familiarity with machine learning techniques (supervised/unsupervised learning, feature selection, anomaly detection) that can include regression and decision tree methods such as Random Forest.

Finance & Trading Knowledge

• Knowledge of quantitative finance models, and risk modeling.
• Experience working with historical datasets for trading, backtesting, and risk management.
• Familiarity with options and futures market structure, volatility analysis, and pricing models.

Product Development & Commercialization

• Strong ability to translate quantitative findings into actionable production engineering results in collaboration with the Cboe Development Teams.
• Strong ability to work with sales, marketing, and business development teams

Preferred Qualifications:

• Ability to apply machine learning in finance, with use cases that could include

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This position is not eligible for visa sponsorship. Candidates must be legally authorized to work in the United States without the need for employer sponsorship now or in the future.

Our pay ranges are determined by a number of factors, including, but not limited to, role, experience, level, and location. The national new hire base pay range for this job in the United States is $134,300-$165,900. This range represents the minimum and maximum base pay the company expects to offer for new hires working in the position full time. If you live in one of the following areas or if you work in a Cboe office in the following areas, the range may be higher according to the geographic differentials listed below:

Within the range, individual pay is determined by a number of factors, including, but not limited to, work location, job-related skills, experience, and relevant education or training. In addition to base pay, our total rewards program includes an annual variable pay program and benefits including healthcare (medical, dental and vision), 401 (k) with a generous company match, life and disability insurance, paid time off, market-leading tuition assistance, and much more!  Your recruiter will provide more details about the total compensation package, including variable pay and benefits, during the hiring process. For further information on our total rewards program, visit TOTAL REWARDS @CBOE.

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 other third party communication tools for recruiting purposes.