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.

• 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.

• 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.