Finance Quant Model Developer, Senior Associate

JPMorgan Chase & Co.•New York, NY

About The Position

The Asset and Wealth Management (AWM) Finance Quantitative Modeling Team uses advanced statistical, quantitative, and computing techniques to develop, implement, test, and conduct analysis on advanced financial forecasting models. The modeled financial outcomes range from investment assets under management and their associated fees to deposit balances to lending portfolio balances and their credit costs. These models support regulatory requirements and inform business decision making. As a Senior Associate on the Asset and Wealth Management Finance Quantitative Modeling Team, you will build, test, and implement advanced forecasting models using statistical and computing techniques to help us meet regulatory requirements and make better business decisions. You’ll work closely with partners in product and risk to translate model outputs into clear insights, analyze large datasets for trends and drivers, proactively identify risks in model assumptions and results, and document methodologies so we can use these tools responsibly and confidently.

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 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.
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.
Prepare detailed documentation of model methodologies, assumptions, and limitations.