Job description

Why Work With Us Solve impactful problems. At Seven Research, our close-knit team unites brilliant minds passionate about pushing technological and scientific boundaries in the global markets. We provide state-of-the-art resources and cultivate intellectual freedom, enabling our people to develop groundbreaking solutions to the most complex challenges of our field. Our collaborative approach celebrates excellence from everyone, regardless of tenure. Your Role Our Quantitative Researchers are the intellectual architects of our trading strategies, responsible for developing sophisticated models that transform complex datasets into actionable insights. You will apply rigorous statistical and machine-learning methodologies to diverse financial datasets, develop innovative approaches to understand risk and returns, and design strategy frameworks leveraging first principles from economics and other fields. What We’re Looking For We seek ambitious and talented problem-solvers with strong quantitative backgrounds. No financial knowledge is expected. Quantitative Researcher candidates should have demonstrated capability in conducting cutting-edge research and analysis. We value team members who communicate clearly, have strong critical analysis skills, and can exercise ownership over projects. If working on challenging quantitative problems in a close-knit setting excites you, we’d love to get to know you. The ideal candidate will possess most of the following: PhD, master’s, or bachelor’s degree in computer science, statistics, physics, or a related quantitative field Intellectual curiosity and passion to uncover the inner workings of the global markets Proven track record developing sophisticated predictive models Strong programming skills, including mastery of Python for data analysis Knowledge of statistical and machine learning modeling frameworks Strong problem-solving skills and research acumen Strong communication skills to articulate complex technical concepts Experience working with large datasets and computationally intensive projects Strong, collaborative mindset and commitment to team success Job Compensation Range The base salary range for this role is $200,000 - $300,000. This does not include other forms of compensation and benefits that are part of Seven Research’s comprehensive package. We will provide additional detail about compensation and benefits during our hiring process. Applicant Privacy Please review our Applicant Privacy Notice for information about how we handle candidate information. Accommodations If you need a reasonable accommodation to participate in any part of our application or interview process, please contact careers@sevenresearch.com . We’ll work with you to support your participation. We are an equal opportunity employer and prohibit discrimination and harassment without regard to race, religion, age, sex, or any other characteristic protected by law. If you have any questions, please contact us at careers@sevenresearch.com .

Requirements

Must-haves

What We’re Looking For
The ideal candidate will possess most of the following:
PhD, master’s, or bachelor’s degree in computer science, statistics, physics, or a related quantitative field
Intellectual curiosity and passion to uncover the inner workings of the global markets
Proven track record developing sophisticated predictive models
Strong programming skills, including mastery of Python for data analysis
Knowledge of statistical and machine learning modeling frameworks
Strong problem-solving skills and research acumen
Strong communication skills to articulate complex technical concepts
Experience working with large datasets and computationally intensive projects
Strong, collaborative mindset and commitment to team success
Job Compensation Range
Applicant Privacy
Please review our Applicant Privacy Notice for information about how we handle candidate information.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonmachine learning

Platform: greenhouse
Effort: quick