The role
From JPMorgan Chase's own posting.
Join our Global Services Insights & Analytics team and help turn data into decisions that improve how we operate. You’ll partner with senior leaders, tackle meaningful business problems, and deliver insights that drive measurable outcomes. This is an opportunity to build scalable analytics solutions, strengthen controls, and elevate service and efficiency across Commercial Banking. If you enjoy hands-on data science and clear storytelling, we’d like to hear from you.
As a Data Scientist in Global Services Insights & Analytics , you will lead data-driven initiatives that strengthen planning, efficiency, service, and controls within Commercial Banking. You will work closely with senior stakeholders to frame the right questions, design sound analytical approaches, and translate results into practical recommendations. You will combine statistical methods and machine learning with strong business context to deliver actionable insights. You will help build repeatable solutions that scale and improve performance over time.
In this role, you’ll help shape how the team measures success and prioritizes improvement opportunities. You’ll balance exploratory analysis with production-ready delivery, and you’ll be expected to communicate clearly to technical and non-technical audiences. You’ll also collaborate across business and functional partners to align on outcomes, timelines, and data requirements.
Job responsibilities
Lead analytics and insight-generation initiatives using hands-on data science techniques
Develop experimental frameworks to support data collection and measurement
Define analytics approaches using business knowledge, statistical methods, and machine learning techniques
Build models and data applications that produce actionable insights
Create clear visualizations and messaging that simplify complex results
Collaborate with business and functional stakeholders on analytics initiatives
Translate business needs into well-scoped data solutions and analytical deliverables
Present insights and recommend next steps to senior stakeholders
Required qualifications, capabilities, and skills
Three years of data science experience (financial services or commercial banking experience preferred)
Proficiency in Python, R, and SQL
Experience applying statistical methodologies to business problems (for example, experimentation, inference, forecasting, or classification)
Experience building machine learning models and translating outputs into actionable recommendations
Experience designing repeatable and scalable data solutions
Experience using analytics tools such as Alteryx or Trifacta
Experience working with distributed data environments such as Hadoop
Experience with data visualization tools such as Qlik Sense, Tableau, or D3
Experience with cloud-based analytics platforms, including Databricks
Strong written and verbal communication skills, including presenting insights to senior stakeholders
Preferred qualifications, capabilities, and skills
Deep understanding of large language model techniques, including agents, planning, and reasoning
Experience building and deploying machine learning models on AWS using tools such as SageMaker and Amazon Elastic Kubernetes Service (EKS)
Advanced knowledge of reinforcement learning or meta learning