Finance Quant Model Developer, Senior Associate

JPMorgan Chase & Co.•New York, NY

About The Position

Join a team that works across multiple efforts and models, often owning end-to-end models and partnering directly with product managers and key stakeholders. They apply strong foundations in economics, statistics, and financial markets to help translate changing macroeconomic and business conditions into clear, decision-relevant insights. As a Quantitative Modeling Lead Senior Associate on the Asset and Wealth Management (AWM) Finance Quantitative Modeling Team, you will use advanced statistical, quantitative, and computing techniques to develop, implement, test, and analyze financial forecasting models. Modeled outcomes span AUM and fee revenue, deposit balances, lending portfolio balances, and credit costs. These models support regulatory requirements and inform business decision-making.

Requirements

Master’s or doctoral degree in a quantitative field such as Mathematics, Statistics, Economics, Finance, or Engineering.
4+ years of experience in developing and implementing quantitative models in a financial, business, and/or post-baccalaureate academic research setting.
Strong understanding of statistical and mathematical modeling and testing techniques.
Strong understanding of industry standard statistical and mathematical modeling tools, e.g., Python, SAS, and Excel.
Strong problem-solving skills and the ability to work with complex cross-sectional and longitudinal datasets.
Familiarity with financial instruments, markets, and risk management practices.
Ability to interpret and communicate quantitative results to non-technical stakeholders.

Responsibilities

Support the design, testing, and implementation of quantitative models for pricing, risk management, and financial forecasting--using Python or similar programming / statistical packages.
Work closely with product and risk managers and other stakeholders to understand business needs, particularly as they relate to interpreting model output.
Identify and assess potential risks associated with model assumptions and outputs while developing strategies to mitigate identified risks.
Analyze large datasets to identify trends, patterns, and insights and use statistical tools and techniques to interpret complex data.
Validate models against historical data and refine them as necessary.
Prepare detailed documentation of model methodologies, assumptions, and limitations.
Collaborate with IT teams to integrate models into existing systems.