Lead Associate Principal, Quantitative Risk Management - Software Engineering role

The Lead Associate Principal, Quantitative Risk Management - Software Engineering role is a highly technical position that combines expertise in quantitative modeling, software engineering, and risk management. The individual will be responsible for developing, implementing, and maintaining risk models used for margin calculations, clearing funds, stress testing, and other financial applications. The role requires collaboration with cross-functional teams, including quantitative analysts, business users, data engineers, and model validators.

Key Responsibilities

Model Development and Implementation:
Develop and maintain quantitative models for pricing, risk management, and stress testing of financial products and derivatives.
Review white papers, implementation notes, and model documentation.
Design, implement, and test model prototypes using best practices.

Testing and Quality Assurance:
Conduct comprehensive QA testing on model implementations, including requirement verification, coding quality, and test automation.
Write test plans, define test cases, and automate unit tests for models.
Troubleshoot and fix bugs in software.

Collaboration and Communication:
Present test plans and results to peers, model validators, and developers.
Participate in code reviews for QRM libraries, prototypes, and tools.
Provide integration support for applications consuming QRM libraries.

Production Support and Backtesting:
Perform large-scale model backtesting using historical data.
Analyze backtesting results and provide insights to risk managers.
Provide production support for numerical libraries and risk management systems.

Tool Development and CI/CD:
Contribute to the development and testing of Model Development Tools, including databases, ETLs, services, orchestration, and CI/CD pipelines.
Proficiency in CICD stack tools (e.g., Jenkins, GitHub, Artifactory).

Support for New Product Launches:
Support the launch of new financial products by ensuring models are accurate and robust.

Required Skills and Qualifications

Analytical Skills
Strong understanding of:
Financial mathematics (derivatives pricing, stochastic calculus, statistics, probability theory, linear algebra).
Econometrics, data analysis (time series, GARCH, fat-tailed distributions, copulas).
Numerical methods, optimization, Monte Carlo simulation, finite difference techniques.
Risk management methods (VaR, expected shortfall, stress testing, backtesting).

Software Engineering Skills

Programming:
Extensive experience in Python for prototyping, efficient coding, and test automation.
Familiarity with design patterns and best practices in Python.

Testing:
Experience with automated QA frameworks (e.g., Pytest, JUnit, TestNG).
Ability to write test plans, define test cases, and perform troubleshooting.

Database:
Proficiency in relational databases and SQL.

CI/CD:
Proficiency in tools like Jenkins, GitHub, and Artifactory.
Communication and Collaboration
Strong interpersonal, verbal, and written communication skills.
Ability to explain technical concepts to non-technical audiences.

Problem-Solving
Ability to identify the root cause of problems, assess severity, and propose solutions.

Education and Experience
Master s degree (or equivalent) in a quantitative field (e.g., computer science, mathematics, physics, finance).
6+ years of experience in quantitative finance or model implementation/testing.

Nice-to-Have Skills
Programming:
Knowledge of other programming languages (e.g., Java, C++, R, shell scripting).

Big Data and Cloud:
Experience with non-relational databases and cloud-based technologies.

Web Development:
Familiarity with web app development using frameworks like FastAPI.

Agile/Scrum:
Experience working in Agile/Scrum environments.

DevOps:
Experience with DevOps practices and tools.

Key Attributes
Analytical: Ability to read and understand white papers on quantitative models and implement them in software.
Detail-Oriented: Precision in coding, testing, and documentation.
Team Player: Collaborative mindset with the ability to work across teams.
Business-Oriented: Understanding of how models impact business outcomes.