Principal Quantitative Researcher

About the position

The Principal Quantitative Researcher at Cboe Global Markets Inc. is responsible for leading quantitative research projects, developing advanced statistical models, and providing strategic guidance to a team of analysts. This role involves writing complex queries to manage datasets, communicating findings to diverse audiences, and collaborating with various stakeholders to build machine learning pipelines. The position requires a strong background in quantitative analysis and programming, with a focus on financial datasets.

Responsibilities

• Write complex queries or codes to collect, combine and clean datasets.
• Communicate research findings and recommendations effectively to diverse audiences, including senior leadership, through reports, presentations and visualizations.
• Spearhead quantitative research projects, providing strategic direction and guidance to a team of analysts.
• Identify research questions and define research methodologies and frameworks, ensuring alignment with business objectives and industry best practices.
• Leverage deep industry knowledge to create, develop and implement advanced statistical models and algorithms using programming languages.
• Analyze complex financial datasets, extract meaningful insights, conduct predictive modeling and create hypothesis testing.
• Create professional white papers and commentaries.
• Collaborate with data engineers, data analysts and business stakeholders to build end-to-end machine learning pipelines.

Requirements

• Master's degree in Statistics, Mathematics, Economics, Financial Engineering, Quantitative Methods or related field.
• 3 years of experience as a Quantitative Analyst, Quantitative Researcher, Data Scientist, Data Analyst or related analytical role.
• 3 years of experience with statistical modeling, hypothesis testing, and data mining techniques.
• 3 years of experience with Python programming.
• 3 years of experience with visualization tools including Matplotlib, Seaborn, Tableau or Sigma.
• 3 years of experience handling large datasets on cloud-based or on-premises databases using tools like Snowflake or Hadoop.
• 3 years of experience with machine learning techniques and frameworks, including TensorFlow, scikit-learn or PyTorch.
• 3 years of experience with financial markets including Equities, Options, or Futures.
• 3 years of experience with version control systems including Git for collaboration and code management.

Benefits

• Telecommuting permitted within the MSA.
• Competitive salary range of $160,000-$210,000 per year.