About the Role

As a Data Scientist Senior Associate, you will play a crucial role in developing predictive models and advanced RAG (Retrieval-Augmented Generation) pipelines. You will collaborate closely with cross-functional teams to extract actionable insights from complex datasets, driving data-driven decision-making across the organization. Your responsibilities will include solving intricate problems, innovating solutions for complex challenges, and building AI-based solutions to improve both technological and business efficiency.

Job Responsibilities

• Align machine learning problem definitions with business objectives to ensure solutions address real-world needs.
• Design, develop, and manage prompt-based models on Large Language Models (LLMs) for complex financial services tasks.
• Architect and oversee the development of next-generation machine learning models and systems using cutting-edge technologies.
• Drive innovation in machine learning solutions, focusing on scalability, flexibility, and future-proofing.
• Promote software and model quality, integrity, and security throughout the organization.
• Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications for enterprise deployment.
• Integrate GenAI solutions with enterprise platforms using API-based methods.
• Establish validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails.
• Collaborate with technology teams to lead the design and delivery of GenAI products.

Qualifications and Skills

• Formal training or certification in software engineering concepts.
• 8+ years of applied AI/ML experience.
• Strong understanding of the Software Development Life Cycle (SDLC), Data Structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, and Statistics.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Proficiency in RDBMS, NoSQL databases, and prompt design.
• Demonstrated expertise in machine learning frameworks such as TensorFlow, PyTorch, pyG, Keras, MXNet, and Scikit-Learn.
• Proficient in building AI Agents (e.g., LangChain, LangGraph, AutoGen), integration of tools (e.g., API), and RAG-based solutions (e.g., open search).
• Experience with Knowledge Graphs (e.g., Neo4j).
• Proven track record of building and scaling software and/or machine learning platforms in high-growth or enterprise environments.
• Exceptional ability to communicate complex technical concepts to both technical and non-technical audiences.

About the Team

The Consumer & Community Banking (CCB) division serves Chase customers with a comprehensive suite of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans, and payment processing. We are a leader in the U.S. for credit card sales and deposit growth, boasting highly utilized digital solutions and top-tier customer satisfaction. The CCB Data & Analytics team leverages data responsibly to create competitive advantages and deliver value and protection to customers. Our team spans data governance, strategy, reporting, data science, and machine learning, working in strong partnership with our Technology colleagues to provide cutting-edge data and analytics infrastructure. We empower Chase with insights to achieve optimal customer and business outcomes.