Quant Analytics Senior associate - Card Data Analaytics
JPMorgan Chase · Wilmington, United States +1 · 2h ago
SeniorOn-siteQuant Research & Development
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.
We're driven by curiosity, passion, optimism, and the belief that everybody can grow.
As a Quantitative Analytics Senior Associate at JPMorganChase within Card Data & Analytics, you will deliver high-impact quantitative insights that shape strategies across cobrand credit card partnerships — from acquisition and engagement to retention and rewards optimization. Your work will directly influence partnership strategies, cardholder value, and customer experience, with meaningful visibility to senior leadership. This is an opportunity to bring your analytical expertise to a collaborative, fast-paced team where your insights drive real business outcomes.
Job responsibilities
- Execute quantitative analytics to drive credit card portfolio strategy and performance across key metrics including acquisition, activation, spend, retention, and attrition
- Develop innovative analytical solutions and frameworks to address complex business challenges across cobrand partnership programs
- Consult on experimental design and testing strategies in collaboration with partners and internal teams to validate and scale initiatives
- Present findings and actionable recommendations clearly and compellingly to senior management and cross-functional stakeholders
- Maintain rigorous data controls to ensure accuracy, integrity, and compliance across all analytical outputs
- Apply advanced analytical tools and models — including generative AI and segmentation frameworks — to uncover actionable insights
- Collaborate closely with marketing, product, and Data & Analytics teams to align analytical work with broader business priorities
- Optimize rewards structures and cardholder value propositions to enhance customer loyalty and engagement
- Prepare and deliver partner reporting and performance insights to support cobrand relationship management
Required qualifications, capabilities, and skills
- Formal training or certification on analytics concepts and 3+ years applied experience
- Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Economics, Computer Science) or an advanced degree with relevant analytical experience
- Proficiency in statistical programming languages including Python, R, or SAS, and data querying languages such as SQL
- Experience with modern analytics and data tools including Hive, Hadoop, Spark, Tableau, or Alteryx
- Exceptional analytical and quantitative problem-solving skills with the ability to translate complex data into clear, actionable insights
- Strong communication skills with the ability to convey complex information to both technical and non-technical audiences
- Demonstrated intellectual curiosity and a proactive approach to solving ambiguous business problems
- Familiarity with generative AI concepts and prompt engineering fundamentals
Preferred qualifications, capabilities, and skills
- 3 or more years of experience in the credit card industry, with exposure to cobrand or partnership programs
- Experience with customer segmentation, lifecycle analytics, or rewards optimization strategies
- Familiarity with experimental design methodologies such as A/B testing or champion/challenger frameworks
- Demonstrated ability to influence strategy through data storytelling and executive-level presentations