JPMorgan Chase—Data Scientist Associate

Job description

Description: Join the Data & Analytics team at JPMorgan Chase, where we leverage data science, research, and business acumen to promote client-focused innovation. As a Data Scientist Associate within Data & Analytics team, you will work on projects that integrate agentic workflows, business intelligence, data engineering, and data visualization to grow our business by translating data into actionable insights. 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. Utilizing tools such as Databricks, Python, Tableau and Alteryx, you will develop data pipelines and dashboards, prototype new generative AI capabilities and features, and ensure the quality and integrity of our intelligent solutions. Your contributions will help streamline operations, enhance decision-making, and foster growth. Job Responsibilities - Leverage your technical skillset and curiosity to identify opportunities to help grow our business - Build and rigorously test new data and AI driven insights - Develop scalable frameworks for seamless AI model integration across business applications. Ensure solutions can adapt and expand as needed. - Build and test AI agents. Iterate designs to enhance functionality and user experience. - Conduct rigorous testing for reliability and effectiveness. - Use tools like Databricks, Python, Tableau and Alteryx 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. - Work on agile teams to support data-driven decision-making and client relationship management. Required Qualifications, Capabilities, and Skills - Degree in a scientific field (Computer Science, Engineering, Data Science, etc.) with 4+ years of experience in AI/ML or data science - Understanding of AI models, including large language model (LLM) capabilities and limitations. - Experience with statistical analysis, data-driven decision-making, and pattern identification. - Creative problem-solving skills. - Experience with Python and SQL. - Exposure to cloud platforms such as AWS, GCP, or Azure, and SDLC concepts. - Strong communication and collaboration skills, with the ability to work effectively in teams. - Willingness to learn and contribute to agentic AI architectures. Preferred Qualifications, Capabilities, and Skills - Experience in data analysis, data science, or related fields. - Familiarity with AWS, Databricks for data management and analysis. - Understanding of quality assurance practices and the importance of data integrity. - Knowledge of machine learning/data science theory, techniques, and tools.