Quantitative Financial Analyst I

Job Summary:

The Quantitative Developer builds, tests, and maintains the financial models, calculation libraries, and data pipelines that power Clearwater’s analytics. This is an early-career opening intended for candidates completing a master’s program in a quantitative field. Quantitative Developers learn Clearwater’s financial models and data model, implement calculations as tested and reviewed code alongside software engineering teams, and grow into ownership of a domain over time. The role blends applied quantitative finance with hands-on software development, and no prior professional experience is required — we expect strong programming fundamentals and a solid quantitative foundation, and will teach the rest.

Responsibilities:

- Assist senior Quantitative Developers and Quantitative Financial Analysts in researching and implementing new calculations as part of larger projects.

- Write clear, tested Python that follows team standards, and contribute to the shared libraries and internal tooling used across the team through the normal code review process.

- Accurately replicate existing mathematical models in Excel and Python, including client analytics tie-outs.

- Perform acceptance, regression, and integration testing of financial models using the existing automated testing frameworks.

- Write, read, and edit SQL queries to extract security, position, and market data for model inputs, validation, and ad-hoc analysis.

- Implement numerical and statistical methods — Monte Carlo simulation, solvers and root-finding, interpolation — under the direction of more senior team members.

- Build and maintain components of the data pipelines that source, normalize, and validate data consumed by financial models.

- Research and learn the data model for your domain, including the data consumed and produced by the code base.

- Assist operations teams in understanding how data inputs impact calculations, and assist developers in analyzing unexpected regressions for a code change.

- Identify and build small automations, including the effective use of AI-assisted development tools, to simplify recurring analytical, validation, and documentation work.

- Proactively update internal documentation to reflect new features and calculation methodology.

- Answer questions within your domain about calculation methodology for internal stakeholders, and communicate findings clearly to non-technical audiences.

- Build domain knowledge continuously, and stay current with quantitative analysis techniques and software engineering practice.

Requirements:

- Master’s degree, completed or to be completed before the start date, in Financial Engineering, Finance, Economics, Engineering, Mathematics, Statistics, Physics, Computer Science, or a similar quantitative field

- No prior professional experience required

- Demonstrated programming ability in Python — evidenced through coursework, thesis work, internships, or personal projects — including writing reusable functions and modules, working with structured data, and implementing financial or mathematical calculations

- Strong quantitative foundation including probability, statistics, linear algebra, and numerical methods

- Foundational understanding of financial markets, instruments, and investment strategies

- Strong written and verbal communication skills, including the ability to explain quantitative work to non-technical audiences

- Receptive to direction and feedback, and willing to escalate roadblocks early

Desired Experience or Skills:

- Exposure to SQL and relational databases

- Familiarity with version control (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 Securities and Risk Analytics, including cash flow analysis, OAS, duration and convexity

- Coursework or research in Stochastic Modeling of Financial Markets

- Interest rate modeling (e.g., Hull-White, HJM, LIBOR Market Model) and model calibration

- Exposure to Derivatives Pricing Models and computing Implied Volatility

- Proficiency with scientific Python libraries (NumPy, pandas, SciPy)

- Experience building data pipelines that source and normalize data from multiple systems or vendors

- Advanced Excel modelling

- Effective use of AI coding assistants and LLM-based tooling within a development workflow

- Familiarity with automated testing frameworks and the software development process, i.e. Agile

- Progress toward or completion of the CFA, FRM, or CQF