Job description

Job Summary: The Quantitative Analyst / Developer designs, builds, and maintains the financial models, calculation libraries, and data pipelines that power Clearwater’s analytics. Quantitative Developers research financial models, products, and techniques, then implement them as production-quality, well-tested code — working alongside software engineering teams to deliver calculations into new and existing software. The role blends applied quantitative finance with hands-on software development, and requires both a working knowledge of asset classes and quantitative modeling techniques (risk analytics, amortization, valuation, and performance) and strong technical skills (Python, SQL, version control) for building and automating analytics on large data sets. Responsibilities: Design, build, and maintain quantitative models, calculation libraries, and data analytics tools used to value portfolios, assess financial risk, and forecast portfolio behavior. Write production-quality Python — modular, reusable, version-controlled, peer-reviewed, and covered by automated tests — and contribute to the shared libraries and internal tooling used across the team. Build and maintain data pipelines that source, normalize, and validate security, market, and reference data consumed by financial models. Develop, maintain, and execute test plans for financial models and their software implementations, including regression and integration testing within the existing automated testing frameworks. Implement numerical and statistical methods — Monte Carlo simulation, solvers and root-finding, interpolation, optimization — and stochastic models, including interest rate models, for valuation, risk, and cash flow analytics. Provide requirements, design, and scope considerations for software development projects involving financial models, and translate model specifications into working implementations. Identify and build opportunities for automation, including the effective use of AI-assisted development tools, to simplify recurring analytical, validation, and documentation work. Write and optimize SQL to extract security, position, and market data for model inputs, validation, and ad-hoc analysis. Independently replicate and tie out existing mathematical models across Excel and Python, and analyze large sets of model output to identify relationships, shortcomings, and improvements. Collaborate with cross-functional teams, including clients, operations, and software engineers, to develop and implement financial technology solutions. Communicate complex quantitative concepts and findings to non-technical stakeholders through clear and concise documentation, reports, presentations, and trainings. Stay current with advancements in financial technology, quantitative analysis techniques, software engineering practice, and industry regulation. Participate in client conversations related to their domain. Requirements: Finance, Economics, Engineering, Mathematics, Statistics, Physics, Computer Science, or similar quantitative degree One of the following: a PhD in a quantitative discipline; a Master’s degree and at least 2 years of relevant experience; or a Bachelor’s degree and at least 3 years of direct experience in quantitative analysis or development Demonstrated programming ability in Python— writing reusable, tested code, interacting with APIs, and managing data for financial modeling and calculations Experience with version control (Git) and collaborative software development workflows Working proficiency in SQL, including writing and editing queries against relational databases Extremely strong analytical skills including statistical concepts, quantitative methods, or risk management techniques Solid understanding of financial markets, instruments, and investment strategies Strong written and verbal communication skills Desired Experience or Skills: Experience in Fixed Income Securities and Risk Analytics including cash flow analysis, OAS, duration and convexity, and numerical methods Experience with Stochastic Modeling of Financial MarketsInterest rate modeling (e.g., Hull-White, HJM, LIBOR Market Model) and model calibration Experience in Derivatives Pricing Models and computing Implied Volatility Advanced Excel modelling Proficiency with scientific Python libraries (NumPy, pandas, SciPy) and performance optimization of numerical code Experience building data pipelines that source and normalize data from multiple systems or vendors Experience with automated testing frameworks, CI/CD pipelines, and code review practice Effective use of AI coding assistants and LLM-based tooling within a development workflow Familiarity with cloud platforms and services used for analytics workloads (e.g., AWS) Familiarity with the software development process, i.e. Agile Progress toward or completion of the CFA, FRM, or CQF

Requirements

Must-haves

Design, build, and maintain quantitative models, calculation libraries, and data analytics tools used to value portfolios, assess financial risk, and forecast portfolio behavior.
Write production-quality Python — modular, reusable, version-controlled, peer-reviewed, and covered by automated tests — and contribute to the shared libraries and internal tooling used across the team.
Build and maintain data pipelines that source, normalize, and validate security, market, and reference data consumed by financial models.
Develop, maintain, and execute test plans for financial models and their software implementations, including regression and integration testing within the existing automated testing frameworks.
Provide requirements, design, and scope considerations for software development projects involving financial models, and translate model specifications into working implementations.
Identify and build opportunities for automation, including the effective use of AI-assisted development tools, to simplify recurring analytical, validation, and documentation work.
Write and optimize SQL to extract security, position, and market data for model inputs, validation, and ad-hoc analysis.
Independently replicate and tie out existing mathematical models across Excel and Python, and analyze large sets of model output to identify relationships, shortcomings, and improvements.
Collaborate with cross-functional teams, including clients, operations, and software engineers, to develop and implement financial technology solutions.
Communicate complex quantitative concepts and findings to non-technical stakeholders through clear and concise documentation, reports, presentations, and trainings.
Stay current with advancements in financial technology, quantitative analysis techniques, software engineering practice, and industry regulation.
Participate in client conversations related to their domain.
Finance, Economics, Engineering, Mathematics, Statistics, Physics, Computer Science, or similar quantitative degree
Demonstrated programming ability in Python— writing reusable, tested code, interacting with APIs, and managing data for financial modeling and calculations
Experience with version control (Git) and collaborative software development workflows
Working proficiency in SQL, including writing and editing queries against relational databases
Extremely strong analytical skills including statistical concepts, quantitative methods, or risk management techniques
Solid understanding of financial markets, instruments, and investment strategies
Strong written and verbal communication skills
Experience in Fixed Income Securities and Risk Analytics including cash flow analysis, OAS, duration and convexity, and numerical methods
Experience with Stochastic Modeling of Financial MarketsInterest rate modeling (e.g., Hull-White, HJM, LIBOR Market Model) and model calibration
Experience in Derivatives Pricing Models and computing Implied Volatility
Advanced Excel modelling
Proficiency with scientific Python libraries (NumPy, pandas, SciPy) and performance optimization of numerical code

Nice-to-haves

None flagged.

Application prep

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

Keywords to mirror

pythonsqlaici/cdllmawsagile

Platform: workday
Effort: long