Analyst, Global Quantitative Research

Job description

Job Purpose

The Quantitative Analyst will join the Global Quantitative Research Group, which designs, implements, and supports enterprise quantitative models and systems. This role blends quantitative research and data science, focusing on model development, risk analytics, and large-scale data engineering. The primary responsibility is to drive quantitative model initiatives for clearing houses while supporting data-driven solutions across multiple business lines.

The candidate must demonstrate strong quantitative and programming skills, deep understanding of financial derivatives, and the ability to manage complex data workflows. Frequent interaction with Risk Management, Technology, and Senior Management is expected.

Responsibilities

Lead research and development of margin, stress testing, and risk management models for clearing houses. Perform quantitative risk analysis and develop solutions across multiple asset classes (interest rate, equity, credit, and commodity derivatives). Conduct data exploration, statistical analysis, and time series modeling to support quantitative research. Build production-quality, data-driven software solutions for model implementation and analytics.

Develop ETL pipelines and data management tools to ensure reliability, efficiency, and quality of large-scale financial datasets. Diagnose and resolve data issues; recommend improvements to data architecture and governance. Define business requirements and specifications for model enhancements and data workflows. Develop and maintain in-house quantitative research platforms and analytics tools. Document and present methodologies, findings, and risk models to stakeholders including regulators, risk committees, and senior management.

Collaborate with technology teams for production implementation and integration of models and data systems. Engage in innovative research in quantitative finance, advanced statistical techniques, and data science. Knowledge and Experience Advanced degree (MSc or PhD) in Mathematics, Statistics, Physics, Quantitative Finance, Data Science, or a related field. Experience in quantitative finance or data science within financial institutions preferred, with proven record in model development or implementation.

Strong programming skills in Python and SQL; experience with R, MATLAB, C++ or Java preferred. Working knowledge of relational databases (Oracle, Postgres, Snowflake) and version control tools (Git). Solid understanding of statistics, time series analysis, and financial derivatives pricing and risk management. Ability to work under pressure in a high-performance environment with tight deadlines. Excellent analytical, organizational, and communication skills; capable of articulating complex concepts to diverse audiences.

Customer-focused, results-oriented, and highly detail-oriented.