Associate - Quant Analytics

Job Description
Join a finance team that supports the Consumer and Business Banking segment with reporting, planning, analytics, and decision-support. In this role, you will help develop and maintain quantitative revenue models and build high-quality datasets that power decisions. You will combine strong technical skills with an understanding of banking products, pricing structures, revenue drivers, and customer behavior. Your work will influence stakeholder outcomes through clear analysis, validated data, and actionable insights.
Job Summary
As a Revenue Finance Quantitative Analytics and Data Engineering Analyst within the Consumer and Business Banking Finance team, you will support the development and maintenance of revenue models through quantitative analytics, data engineering, and model implementation. You will prepare and validate large datasets, perform model testing and back-testing, and evaluate model performance to support business objectives. You will apply knowledge of banking products, pricing structures, revenue drivers, and customer behavior to deliver meaningful insights to internal stakeholders, including Finance and Business Management leaders and field finance teams. You will partner across Technology, Finance, and Business teams to translate requirements into analytical solutions and support successful deployment. This role follows a 2:00pm to 11:00pm shift and works in a hybrid in-office model.
Job responsibilities
Develop and support quantitative revenue models by preparing, validating, and transforming large datasets using Databricks, while ensuring data quality through root cause analysis and issue resolution
Implement, test, and back-test quantitative models within cloud-based and analytical environments using Python, ensuring model accuracy, robustness, and scalability
Evaluate model performance, analyze variances, and communicate findings through clear, data-driven recommendations that support business objectives
Leverage deep understanding of Consumer and Business Banking products, pricing structures, and revenue drivers to support analytical and strategic decision-making
Conduct advanced analytics and customer behavior deep dives to identify business opportunities, uncover trends, and generate actionable insights for stakeholders
Partner effectively across Technology, Finance, and Business teams to translate business requirements into analytical solutions and drive successful model deployment
Prepare and deliver executive-level presentations that effectively communicate complex quantitative, financial, and technical concepts to senior leadership and key stakeholders
Required Qualifications, Capabilities, And Skills
Bachelor's degree required
Advanced proficiency in SQL, Python, and Databricks, with experience in data preparation, transformation, validation, and quantitative model implementation
Experience working with large, complex datasets and deriving actionable business insights
Strong analytical and problem-solving skills
Minimum 2 to 4 years of experience in quantitative analytics, data science, and financial modeling
Understanding of banking products, pricing structures, revenue drivers, and customer behavior
Ability to evaluate model performance, analyze variances, and communicate data-driven recommendations
Strong collaboration skills and ability to partner across Technology, Finance, and Business teams
Ability to prepare and deliver executive-level presentations communicating complex quantitative, financial, and technical concepts
Attention to detail, critical thinking, and ability to manage multiple priorities in a fast-paced team environment
Positive attitude and intellectual curiosity
Preferred Qualifications, Capabilities, And Skills
Master of Science, Master of Technology, or Master of Business Administration (Finance)
Experience with cloud-based analytics platforms
Familiarity with AI tools, Tableau, and Alteryx
Familiarity with financial modeling
ABOUT US
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
About The Team
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction.
The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

Work arrangement
No