Quant Analytics Associate - Wealth Management

Leverage your technical expertise to shape innovative solutions and align capabilities to solve real-world challenges.
As a Quant Analytics Associate at JPMorganChase within the Solutions and Advice Analytics team in US Wealth Management, you will blend cross-functional knowledge across financial products, markets, statistics, and advanced analytics to help inform decisions that improve client outcomes. You will work in close collaboration with stakeholders across functions, translating complex analyses into clear narratives and actionable recommendations.
Job responsibilities
Analyze client, advisor, and macro-behavioral trends related to wealth management products and solutions, including managed solutions, mutual funds, exchange-traded funds, banking, and lending
Analyze financial flow data to inform strategic decision-making, including forming hypotheses and conducting deep-dive investigations
Provide analytical support for regulatory matters through well-documented, high-quality analysis and responsive stakeholder partnership
Research competitor positioning using data-driven approaches to support strategic discussions and business decisions
Assemble, organize, validate, and analyze data using tools and technologies such as SQL, Python, and R
Apply statistical, causal inference, and machine learning techniques, including clustering, tree-based models, matching methods, and time-series anomaly detection
Present findings and recommendations to stakeholders and senior leaders across Solutions and Advice, Finance, Legal, and Data and Analytics
Required qualifications, capabilities, and skills
1+ years of experience in an analytical, analytics, data science, finance, or management and economic consulting role
Bachelor’s degree in a related field such as Mathematics, Statistics, Engineering, Computer Science, Finance, Economics, or another applicable science, technology, engineering, or mathematics discipline
Strong SQL skills with experience using SQL to assemble, organize, and analyze datasets
Experience using Python and/or R for analysis, modeling, and data preparation
Ability to explain complex concepts in clear, digestible terms for audiences at various levels of the business
Strong storytelling skills, including turning analysis into actionable recommendations
Aptitude for learning new theory and new technology in a fast-moving environment
Demonstrated attention to detail and strong quality standards in analytics deliverables
Preferred qualifications, capabilities, and skills
Master’s degree or PhD
Knowledge of or demonstrable interest in financial securities and markets
Practical understanding of advanced statistics, econometrics, and/or machine learning
Practical understanding of financial modeling and economics
Experience applying causal inference methods in an applied business setting