Finance Decision Optimization - Quant Analytics Associate

The Finance Decision Optimization team (FDO) is a group of product, analytics, and engineering experts who support the development of forecasting models used to drive strategic decisions across CCB Finance. As a Quant Analytics Associate, within the Deposits Area Product team, you will use multifaceted analytics to support the end-to-end development of a suite of deposit models that support pricing, treasury, and finance functions. Job Responsibilities: Drive business outcomes and decision-making through data and analytics, leveraging a data warehouse to inform modeling approaches, understand customer behavior, and perform sensitivity analyses. Develop comprehensive knowledge of supported business units to deliver pragmatic and effective solutions. Translate business demands into technical requirements documents and collaborate with technology teams. Work closely with end users and data product owners during the UAT phase, and perform testing to ensure new functionality meets end-user requirements. Partner with the business modeling team to develop and refine statistical models, while continuously evaluating performance and effectiveness. Validate that production results comply with business requirements and expected outcomes. Translate business requirements into prototype solutions for new requests and enhancements. Effectively manage multiple tasks and priorities in a fast-paced environment while remaining responsive to ad hoc requests. Required qualifications, capabilities, and skills: Strong problem solver with excellent analytical, critical thinking, communication, organizational, and technical skills; proven ability to collect, organize, and analyze significant amounts of information while maintaining attention to detail and accuracy. 2+ years of experience at a financial institution or consulting firm in one or more of the following areas: corporate finance, banking, treasury, data analytics, or quantitative modeling. Proficiency in business analytics tools (e.g., SQL, SAS, Python, R) and/or programming languages used to perform data analytics and drive business outcomes. Ability to communicate effectively with a variety of technical peers (including data engineering and quantitative modeling teams) and to translate data into concise, actionable recommendations. Preferred Qualifications: Experience with large-scale data projects is preferred. Experience with Databricks, Streamlit, and AWS cloud environments is preferred. #J-18808-Ljbffr JPMorgan Chase & Co.