Quantitative Financial Analyst I

Clearwater Analytics•Boise, ID

About The Position

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.

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

Nice To Haves

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

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.