JPMorgan Chase
Quantitative Analytics Vice President – Co-Brand Card Analytics
$119K–$205K
Wilmington, DE +1 · Director · Full-time · Posted Oct 12, 2026
- Location
- Wilmington, DE
Brooklyn, NY - Experience
- 5+ years of applied experience in software engineering concepts, plus either 5+ years of data analytics experience with a relevant bachelor's degree or 2+ years of experience with an advanced degree.
- Education
- Bachelor's degree in a relevant quantitative field, or an advanced degree.
About the job
Develops analytics and AI/ML solutions for the Co-Brand Credit Card business, translating business questions into models and actionable insights that support strategy and measurable impact.
What you'll do
- Define business problems with partners and design, build, and evaluate analytical approaches and models, including Generative AI methods.
- Develop analytics strategies and products, explore diverse datasets, and communicate findings and obstacles to stakeholders.
- Collaborate across teams to deliver solutions and develop expertise in financial and operational domains.
What they ask for
- Formal training or certification in software engineering concepts and 5+ years of applied experience.
- Bachelor's degree in a relevant quantitative field and 5+ years of data analytics experience, or an advanced degree and 2+ years of experience.
- Knowledge of statistical software and data querying languages, such as Python, R, SAS, or SQL; experience with modern analytics tools.
- Familiarity with Generative AI and prompt-engineering basics, including prompt design, evaluation, and guardrails.
Nice to have
- Experience with LLM-enabled applications and evaluation methods for quality, cost, and latency.
- Understanding of credit card profit-and-loss drivers; financial services background; or a Master of Science degree or equivalent.
Pay
USD 118,750.00-$195,000.00 base salary; USD 133,000.00-$205,000.00 base salary
Tools
Python, R, SAS, SQL, Hive, Hadoop, Spark, Tableau
AI focus
Develop and evaluate analytics and AI/ML solutions, including responsible use of foundation models and Generative AI; familiarity with prompt engineering. Preferred work includes LLM applications such as retrieval-augmented generation, classification or extraction from unstructured text, and agent-like workflows.
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