What are the responsibilities and job description for the Data Scientist Associate - Payments position at JPMorgan Chase?
Join the JPM Payments Data & Analytics' Merchant Services Cost Insights team at JPMorganChase, where we leverage data science, research, and business acumen to promote client-focused innovation. Apply your technical skills to projects involving cost-based analytics and cutting-edge GenAI and agentic AI applications, creating impactful solutions for Chase Merchant Services clients.
As a Data Science Associate in our Merchant Services Cost Insights team within the Commercial Investment Bank Global Payments team, you will play a pivotal role in developing AI-driven models and GenAI/agentic AI solutions that automate repeatable tasks, creating efficiencies in business processes to drive value and growth. You’ll engage in projects that integrate data science, research, and business domain expertise, focusing on cost-based analytics, business intelligence, data engineering, and visualization.
Your work will involve leveraging your technical skills and business acumen to collaboratively design and implement end-to-end solutions with tangible commercial impact for our clients and customers, including chat assistants and workflow automations powered by LLMs and multi-agent patterns. Our team supports both internal Payment Insights and external Digital Merchant Insights applications, as well as custom client analytics requests on an ad hoc basis.
Utilizing tools such as Snowflake, Python, and Tableau, you will develop data pipelines and dashboards, prototype and iterate on new generative AI capabilities and features, and ensure the quality, integrity, and reliability of our intelligent solutions through testing, evaluation, and monitoring. Your contributions will help streamline operations, enhance decision-making, and foster growth through innovative AI applications.
Job Responsibilities
• Creation of chat assistants using multi-agent AI workflows.
• Conceptualize solutions that automate and optimize business processes.
• Design and refine prompts to improve AI model performance. Work with subject matter experts to align prompts with business goals and user needs.
• Provide merchants with actionable recommendations to enhance their cost strategies.
• Iterate designs to enhance functionality and user experience. Conduct rigorous testing for reliability and effectiveness.
• Use tools like Snowflake, Python, and Tableau to create data pipelines and dashboards. Support AI-driven insights and decision-making.
• Monitor AI model performance. Identify areas for enhancement. Implement updates to maintain quality and relevance.
• Partner with data owners, department managers, data engineers and other stakeholders to aid in the development of data models and algorithms, including requirements and success metrics for GenAI/agentic workflows.
• Support internal Payment Insights and external Digital Merchant Insights applications, as well as custom client analytics requests on an ad hoc basis.
Required Qualifications, Capabilities, and Skills
• Bachelor’s or Master’s Degree in Science, Technology, Engineering, Mathematics (STEM) or a related field.
• Proficiency in Python and SQL for data processing and analysis.
• Minimum 1 year relevant work experience.
• Expertise in quantitative research techniques and analytics using Python.
• Proven critical thinking and creative problem-solving skills with ability to translate strategy into deliverables.
• Knowledge of machine learning/data science theory, techniques, and tools.
• Experience in leveraging Large Language Models (LLMs) and agent AI techniques.
• Ability to collaborate effectively and cultivate strong partnerships.
Preferred Qualifications, Capabilities, and Skills
• Experience in data analysis, data science, or a related field.
• Ability to perform rapid prototyping in Tableau and streamlit.
• Familiarity with AWS, Snowflake for data management and analysis.
• Attention to detail with a proven ability to learn new concepts and skills quickly.
• Understanding of quality assurance practices and the importance of data integrity.
• Ability to clearly develop and document analytic requirements with stakeholders.
• Familiarity with evaluation/monitoring concepts for GenAI solutions (e.g., test cases, human-in-the-loop feedback, basic quality metrics) to support reliable deployments.