Quantitative Associate, Risk Analytics

Morgan Stanley•New York, NY

1d•$100,000 - $140,000•Hybrid

About The Position

Morgan Stanley's Firm Risk Management (FRM) Division is an exciting and rapidly growing space. The division supports Morgan Stanley to achieve its business goals by partnering with business units across the Firm to realize efficient risk-adjusted returns, acting as a strategic advisor to the Board and protecting the Firm from exposure to losses as a result of credit, market, liquidity, operational, model and other risks. The role will reside within the Risk Analytics department in the Firm Risk Management division. Risk Analytics develops market risk, credit risk, and scenario analytics models, providing quantitative analysis of the Firm's risk exposures through mathematical and statistical techniques. Morgan Stanley is seeking an Associate/Analyst in its Market Risk Analytics group. The team develops, maintains, and monitors the performance of market risk models (e.g., VaR, Stressed VaR, IRC) and stress testing frameworks across asset classes, in line with regulatory requirements and internal risk management needs. The successful candidate will join a high-impact team working across model development, implementation, and analysis, with a focus on one or more of the following areas: Market shock scenario design, stress testing and driving AI adoption, including scenario generation and stressed risk measurement; Commodity products market risk modeling, including quantitative analysis, capital calculation, and regulatory frameworks such as FRTB. The role offers exposure to the full model lifecycle, including development, calibration, implementation, validation, performance monitoring, and regulatory engagement. The position also involves opportunities to enhance existing processes through automation and AI-driven solutions, contributing to the evolution of the Firm's risk management capabilities.

Requirements

Requires a degree in Quantitative Finance, Math, Statistics, Computer Science, Physics, Engineering, Economics or a related field of study (Masters/PhD highly preferred)
Strong Quantitative skills
Strong Python coding skills (essential), knowledge of database querying functionalities/languages
Familiarity with statistical modelling, Monte Carlo, Historical Simulation
Knowledge and broad interest in financial products, markets, and risk management and regulations
Strong skills in communication, critical thinking, problem solving, and collaboration

Nice To Haves

Knowledge of financial products with Commodity products will be preferred
Familiarity with stress testing frameworks and scenario design methodologies is preferred
Familiarity with AI tools and understanding of their strengths, limitations, and practical applications is preferred
Experience in developing or deploying analytical or AI tools is preferred

Responsibilities

Performing quantitative analysis on various aspects of Market Risk models like VaR, Stressed VaR, Risk Not in VaR for Commodity products (e.g. Power, Gas, Oil, etc.)
Developing and enhancing models for changing internal risk management needs, new regulatory requirements (e.g., FRTB), or improvements in capturing the risk
Supporting the design and implementation of market shock scenarios and stress testing methodologies, including scenario generation and stressed risk measurement
Identifying opportunities to enhance existing processes through automation and AI tools, and contributing to their development and deployment
Actively participating in code development for the purpose of model implementation, model performance monitoring, and for performing different analyses
Analyze, understand, and explain changes in risk metrics driven by model updates and position changes
Analyzing model performance metrics
Interacting with stakeholders from various departments like Front Office strategists, Market Risk Managers, Model Risk Management and FRM IT
Participating in documentation of model methodologies and implementation
Responding to queries from Model Risk Management, Internal Audit, and regulators