Quantitative Financial Analyst I

Role overview

Responsibilities

• Research and implement calculations with support from senior quantitative developers and analysts.
• Write tested Python and contribute to shared libraries and internal tools through code review.
• Replicate mathematical models in Excel and Python, including client analytics tie-outs.
• Perform acceptance, regression, and integration testing of financial models.
• Query security, position, and market data with SQL for model inputs, validation, and analysis.
• Implement numerical and statistical methods such as Monte Carlo simulation, solvers, root-finding, and interpolation.
• Build and maintain data-pipeline components that source, normalize, and validate model data.
• Help operations teams understand data inputs and calculations, and help developers investigate code-change regressions.
• Build small automations for recurring analysis, validation, and documentation work, including appropriate AI-assisted tools.
• Maintain documentation, answer internal questions about calculation methods, and communicate findings to non-technical stakeholders.

Required qualifications

• Master’s degree completed or expected before the start date in a listed or similar quantitative field, including Financial Engineering, Finance, Economics, Engineering, Mathematics, Statistics, Physics, or Computer Science.
• No prior professional experience is required.
• Demonstrated Python ability through coursework, thesis work, internships, or personal projects, including reusable functions and modules, structured data, and financial or mathematical calculations.
• Strong foundation in probability, statistics, linear algebra, and numerical methods.
• Foundational understanding of financial markets, instruments, and investment strategies.
• Strong written and verbal communication, ability to explain quantitative work to non-technical audiences, and receptiveness to feedback.

Desired experience and skills

• Exposure to SQL and relational databases; familiarity with Git and collaborative software development workflows.
• Internship, co-op, or research experience in financial services, fintech, or quantitative research.
• Coursework or research in fixed income, risk analytics, stochastic modeling, interest rate models, derivatives pricing, or implied volatility.
• Familiarity with NumPy, pandas, SciPy, data pipelines, automated testing, Agile, advanced Excel modeling, or AI coding assistants.

Career development