JPMorgan Chase
Data Science Vice President - Card Data Analytics
$128K–$215K
Wilmington, DE +1 · Director · Full-time · Posted May 7, 2026
- Location
- Wilmington, DE
New York, NY - Experience
- 5+ years applied software engineering experience; either 5+ years of data analytics experience with a relevant quantitative bachelor’s degree, or 2+ years with an advanced degree.
- Education
- Bachelor’s degree in a relevant quantitative field, or an advanced degree. Preferred: Master of Science degree or equivalent.
About the job
Develops data science and AI/ML solutions for the Credit Card business, translating business needs into analytical products, models, and insights that support strategy and measurable impact.
What you'll do
- Define business problems with partners and research, build, and evaluate analytical approaches and models, including Generative AI methods.
- Drive analytics strategies and standards, explore diverse datasets, and communicate findings to stakeholders.
- Collaborate across teams to deliver scalable solutions and actionable insights.
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; 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 or financial services; Master of Science degree or equivalent.
Pay
USD 128,250.00-$200,000.00 base salary; USD 147,250.00-$215,000.00 base salary
Tools
Python, R, SAS, SQL, Hive, Hadoop, Spark, Tableau
AI focus
Responsible use of foundation models and Generative AI; design, evaluation, and implementation of AI/ML solutions, including LLM-enabled applications, prompt engineering, and evaluation of quality, cost, and latency.
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