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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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As a Data Scientist Lead-Vice President on the Business Banking Lending Product Analytics team, you will serve as a trusted analytics partner to help shape small business lending products. You will collaborate with stakeholders from Risk, Finance, and Technology to translate complex data into executive-ready insights and actionable recommendations. Your daily responsibilities include framing high-impact questions, building defensible metrics, developing scalable reporting frameworks, and performing deep-dive analyses across the lending lifecycle. To succeed, you must utilize SQL, Python or R for data transformation, and perform statistical analysis including hypothesis testing and predictive modeling. You will also leverage AI tools to accelerate insight generation while maintaining data quality in a regulated environment. Your work focuses on understanding customer behavior and identifying growth opportunities within the specific domain of business banking lending products and processes.
Translate complex data into clear, actionable recommendations for senior leadership.
Partner with stakeholders to frame high-impact business questions and define measurement approaches.
Build and validate datasets to create defensible metrics and reporting frameworks.
Develop end-to-end analyses covering the entire lending lifecycle from problem framing to insight delivery.
Establish repeatable KPI logic and documentation to ensure auditability and consistent measurement.
Identify opportunities for automation and AI-assisted improvements in analytical workflows.
Analyze customer behavior to identify growth and experience opportunities for lending products.
What we're looking for
Bachelor's degree in a quantitative field such as Computer Science, Engineering, Statistics, Economics, or a related discipline.
5+ years of experience in advanced analytics roles.
Strong skills in SQL and modern analytics programming languages like Python or R for data querying, transformation, and analysis.
Proficiency in statistical analysis and product/customer analytics methods including hypothesis testing, predictive modeling, and experimentation design.
Ability to translate ambiguous business questions into structured analytical approaches and executive-ready storytelling.
Experience with on-prem and cloud data platforms such as Teradata, Oracle, AWS, or Snowflake.
Competent use of AI tools to accelerate insight generation in a regulated environment.
Familiarity with Business Banking lending products, experimentation, forecasting, or working in a regulated environment (preferred).
Information Architecture
Data Management
NoSQL
ETL
Data Lakes
Data Governance
Multi-cloud
AI
Site Reliability Engineering
IT Service Management
Configuration Management