Join the Finance Decision Optimization team, a group of product, analytics, and engineering experts, who support the development of predictive models used to drive strategic decisions across Consumer and Community Banking Finance.

As a Quant Analytics Senior Associate, within the Finance Decision and Deposits Area team, you will use multi-faceted analytics to support the end-to-end development of the deposit pricing model, which is a framework used to help support deposit pricing decision making.

Job Responsibilities:

• Partner with the business modeling team to develop and refine statistical models while continuously evaluating their performance and effectiveness.
• Execute model forecasts and socialize results to business partners and stakeholders to help drive pricing strategy.
• Translate business demands to technical requirement documents and collaborate with technology teams.
• Work closely with end-users and data product owners to build datasets for model training and serving.
• Independently design, develop, and optimize complex SQL queries and Python scripts to support data validation, exploration, and integration with existing systems and codebases.
• Effectively manage multiple tasks and priorities in a fast-paced environment while maintaining responsiveness to ad-hoc requests.

Required qualification, capabilities, and skills:

• Strong problem solver with excellent analytical, critical thinking, communication, organizational, and technical skills.
• 5+ years of experience at a financial institution or consulting firm in one or a combination of the following areas: corporate finance, banking, treasury, data analytics, or quantitative modeling.
• Able to communicate effectively with a variety of technical peers including data engineering and quantitative modeling teams.

Preferred qualification, capabilities, and skills:

• Proficiency in business analytics tools or programming languages (e.g. SQL, SAS, Python, R). Experience with Databricks, Streamlit, and AWS Cloud environments is a plus.
• Preference to those with experience dealing with large scale data projects.