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Finance Decision Optimization - Quant Analytics Associate
Data Science & Analytics
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.