Quant Analytics, Senior Associate - Asset Wealth Management

AI is only as good as the data and business context behind it—and in asset management, that starts with a trusted view of our clients. Join a team that turns complex data into clear insights, strategic recommendations, and reusable analytics products. You will help shape how client data is prepared for AI and how teams across the business use data to make better decisions. If you enjoy combining quantitative rigor with real-world business impact, this role is built for you. As a Senior Associate, Quantitative Analytics at JPMorganChase within the Asset & Wealth Management Business Intelligence & Analytics team, you will own the Client Analytics Data Product and shape how client data is prepared for AI as part of the Asset & Wealth Management AI data strategy. You will partner with marketing, sales, finance, product strategy, client service, and investment teams to understand how each uses client data, and turn those perspectives into a shared view of the client, quantitative insights, and strategic recommendations. You will combine advanced analytics, internal and third-party data, and dashboards in Microsoft Power BI and Qlik Sense to deliver trusted, AI-ready data products that improve business performance. Job responsibilities Own the Client Analytics Data Product roadmap, success measures, and delivery plan, prioritizing work by business use case and adoption outcomes. Partner with marketing, sales, finance, product strategy, client service, and investment teams to run intake, align priorities, and drive adoption of analytics products. Deliver quantitative insights and senior-ready recommendations that improve decisions across the business (for example, segmentation, coverage strategy, profitability, retention, and flows). Design and execute analyses using methods such as regression, segmentation, propensity modeling, attrition modeling, forecasting, attribution, and test-and-learn experimentation. Define shared client and product definitions and certified metrics so teams and AI tools consistently get the same answer to the same question. Build scalable dashboards and reusable analytics products in Microsoft Power BI and Qlik Sense, and partner with technology teams to embed insights into workflows and reporting. Integrate internal and third-party data and build validated analytical models in SQL and Python, addressing entity resolution, hierarchies, history, granularity, and data quality. Required qualifications, capabilities and skills 3+ years of experience in quantitative analytics, data science, business intelligence, or analytics consulting delivering insights and analytics products for multiple business teams. Demonstrated quantitative skills in statistics, regression, segmentation or clustering, forecasting, and experimental design. Advanced SQL and Python experience (for example, pandas, scikit-learn, statsmodels) working with large, multi-source datasets. Hands-on experience building dashboards and analytics products using Microsoft Power BI and/or Qlik Sense. Proven ability to translate analysis into clear, actionable recommendations for senior stakeholders, supported by concrete examples and outcomes. Strong stakeholder management skills, including influencing without direct authority, aligning competing priorities, and driving adoption across teams. Working knowledge of data governance fundamentals (definitions, lineage, data quality, access controls) and how trusted data supports responsible AI use cases. Preferred qualifications, capabilities and skills Experience using third-party asset management datasets such as Morningstar, FINTRX, Broadridge, and/or Preqin for competitive and industry benchmarking. Knowledge of asset and wealth management products and distribution, including mutual funds, exchange-traded funds, separately managed accounts, alternatives, assets under management, and flows. Experience applying advanced methods such as propensity modeling, next-best-action modeling, causal inference, and/or time-series forecasting to client and product datasets. Advanced dashboarding and semantic modeling skills (for example, Data Analysis Expressions, Power Query, and Qlik scripting and set analysis), with familiarity in Snowflake, Databricks, or dbt as a plus.