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:
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Assist senior Quantitative Developers and Quantitative Financial Analysts in researching and implementing new calculations as part of larger projects.
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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.
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Accurately replicate existing mathematical models in Excel and Python, including client analytics tie-outs.
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Perform acceptance, regression, and integration testing of financial models using the existing automated testing frameworks.
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Write, read, and edit SQL queries to extract security, position, and market data for model inputs, validation, and ad-hoc analysis.
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Implement numerical and statistical methods — Monte Carlo simulation, solvers and root-finding, interpolation — under the direction of more senior team members.
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Build and maintain components of the data pipelines that source, normalize, and validate data consumed by financial models.
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Research and learn the data model for your domain, including the data consumed and produced by the code base.
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Assist operations teams in understanding how data inputs impact calculations, and assist developers in analyzing unexpected regressions for a code change.
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Identify and build small automations, including the effective use of AI-assisted development tools, to simplify recurring analytical, validation, and documentation work.
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Proactively update internal documentation to reflect new features and calculation methodology.
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Answer questions within your domain about calculation methodology for internal stakeholders, and communicate findings clearly to non-technical audiences.
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Build domain knowledge continuously, and stay current with quantitative analysis techniques and software engineering practice.
Requirements:
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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
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No prior professional experience required
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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
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Strong quantitative foundation including probability, statistics, linear algebra, and numerical methods
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Foundational understanding of financial markets, instruments, and investment strategies
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Strong written and verbal communication skills, including the ability to explain quantitative work to non-technical audiences
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Receptive to direction and feedback, and willing to escalate roadblocks early
Desired Experience or Skills:
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Exposure to SQL and relational databases
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Familiarity with version control (Git) and collaborative software development workflows
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Internship, co-op, or research experience in financial services, fintech, or quantitative research
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Coursework or research in Fixed Income Securities and Risk Analytics, including cash flow analysis, OAS, duration and convexity
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Coursework or research in Stochastic Modeling of Financial Markets
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Interest rate modeling (e.g., Hull-White, HJM, LIBOR Market Model) and model calibration
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Exposure to Derivatives Pricing Models and computing Implied Volatility
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Proficiency with scientific Python libraries (NumPy, pandas, SciPy)
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Experience building data pipelines that source and normalize data from multiple systems or vendors
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Advanced Excel modelling
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Effective use of AI coding assistants and LLM-based tooling within a development workflow
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Familiarity with automated testing frameworks and the software development process, i.e. Agile
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Progress toward or completion of the CFA, FRM, or CQF