Summer Intern - Quantitative Risk Management
About The Position
The Model Performance Monitoring (MPM) team is responsible for the ongoing monitoring, testing, and governance of the firm's risk models, ensuring model performance remains sound, well-documented, and compliant with regulatory expectations. The team's core functions include: Regulatory & Governance Reporting — Preparing and delivering all regulatory and governance materials (e.g., STANS, CST, SEC, PFMI, and XCME reporting; margin backtesting; sensitivity analysis) on time and to a consistent quality standard. Model Monitoring Framework — Maintaining and continuously strengthening the MPM framework, including data consistency across inputs, metric backtesting and threshold calibration, exceedance/breach investigation, and remediation of findings. Platform Modernization — Migrating and refactoring monitoring workflows onto the Ovation platform, including framework refactoring, environment upgrades, and BUAT testing, to support the firm's long-term technology strategy. AI & Process Automation — Leveraging AI tools (including Claude) and modernized codebases (migrating legacy R/MATLAB to Python, Alteryx to Airflow) to automate repetitive processes, improve turnaround time, and free up capacity for higher-value analytical work. BAU & Production Support — Ensuring day-to-day operational stability across backtesting, production tools, and job scheduling infrastructure, while supporting Model Risk Management (MRM) observation remediation and annual validation cycles. Together, these functions position MPM as the team accountable for the accuracy, reliability, and regulatory readiness of model performance oversight across the firm. Planned responsibilities and learning objectives include: Supporting model performance/backtesting exercises using historical portfolio data, under supervision of senior team members Contributing to Python scripting (Pandas) for data processing and automation of monitoring workflows Assisting with exploratory data analysis and visualization (e.g., Tableau/Dash) of model performance metrics Reviewing and helping draft documentation for model monitoring metrics and testing procedures Participating in code reviews and team discussions on model monitoring best practices
Requirements
- Rising senior or second-year graduate student, graduating December 2027 or May/August 2028.
- Master’s degree or equivalent in a quantitative field such as data analytics, computer science, mathematics, physics, finance/financial engineering.
- Strong programing skills. Able to read and/or write Python code in a collaborative software development setting.
- Experience with a source code repository system (preferably Git)
- Ability to write and optimize complex analytical (SELECT) SQL queries
- Comfortable supporting business analysts on high-priority projects.
- Strong problem-solving skills: Be able to accurately identify a problem's source, severity, and impact to determine possible solutions and needed resources.
- Experience in office technology such as PowerPoint, Confluence, Word, and Excel.
Nice To Haves
- Exposure to data orchestration tools, such as Airflow
- Experience with Tableau, Dash and Alteryx
- Understanding of applied statistics and hands-on experience applying these concepts
- Strong quantitative skills, ability to demonstrate deep understanding in the following technical areas: Financial mathematics (derivatives pricing models, stochastic calculus, statistics and probability theory, advanced linear algebra) Econometrics, data analysis (e.g., time series analysis, GARCH, fat-tailed distributions, copula, etc.) and machine learning techniques Numerical methods and optimization; Monte Carlo simulation and finite difference techniques Risk management methods (value-at-risk, expected shortfall, stress testing, backtesting, scenario analysis) Financial products knowledge: good understanding of markets and financial derivatives in equities, interest rate, and commodity products.
Responsibilities
- Perform model performance testing, including portfolio back-testing using historical data.
- Implementation of model monitoring metrics, focusing on requirement coding, and testing quality
- Write and review documentations for model monitoring metrics, prototypes and implementation
- Support the launch of new products by enhancing monitoring capabilities.
- Develop Python scripts with Pandas and object-oriented programming to automate data processing
- Assist analysts in solving their analytics questions/challenges
Benefits
- Paid sick leave accrued based on hours worked
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What This Job Offers
Career Level
Intern
